Method for determining relationships through use of an ordered list between processing nodes in an emulated human brain

ABSTRACT

A method for emulating human cognition in electronic form is disclosed. Information is received in the form of a textual or voice input in a natural language. This is parsed into pre-determined phrases based on a stored set of language rules for the natural language. Then, the parsed phrases are determined as to whether they define aspects of an environment and, if so, then creating weighting factors to the natural language that are adaptive, the created weighting factors operable to create a weighted decision based upon the natural language. Then it is determined if the parsed phrases constitute a query and, if so, then using the weighted factors to make a decision to the query.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Patent ApplicationSer. No. 60/811,299, filed Jun. 5, 2006, and entitled THE CONCEPT OFGAMUT AND ITS USE, and is a Continuation-in-Part of pending U.S. patentapplication Ser. No. 11/697,721, filed Apr. 7, 2007, and entitled METHODFOR SUBSTITUTING AN ELECTRONIC EMULATION OF THE HUMAN BRAIN INTO ANAPPLICATION TO REPLACE A HUMAN. Application No. 11/697,721 is aContinuation-in-Part of pending U.S. patent application Ser. No.11/670,959, filed Feb. 2, 2007, and entitled METHOD FOR MOVIE ANIMATION.Application Ser. No. 11/670,959 is a Continuation-in-Part of U.S. patentapplication Ser. No. 11/425,688, filed Jun. 21, 2006, and entitledMETHOD OF EMULATING HUMAN COGNITION IN A BRAIN MODEL CONTAINING APLURALITY OF ELECTRONICALLY REPRECENTED NEURONS, now U.S. Pat. No.7,849,034, issued on Dec. 7, 2010. Application Ser. No. 11/670,959 alsoclaims benefit of U.S. Provisional Patent Application Ser. No.60/764,442, filed Feb. 2, 2006, and entitled USE OF THE NEURIC BRAINMODEL IN MOVIE ANIMATION. Application Ser. No. 11/425,688 is aContinuation of U.S. application Ser. No. 11/154,313, filed Jun. 16,2005, and entitled METHOD FOR INCLUSION OF PSYCHOLOGICAL TEMPERAMENT INAN ELECTRONIC EMULATION OF THE HUMAN BRAIN, now U.S. Pat. No. 7,089,218,issued Aug. 8, 2006. Application Ser. No. 11/154,313 is a Continuationof abandoned U.S. application Ser. No. 11/030,452, filed Jan. 6, 2005,and entitled METHOD FOR INCLUSION OF PSYCHOLOGICAL TEMPERAMENT IN ANELECTRONIC EMULATION OF THE HUMAN BRAIN. Application Ser. No. 11/030,452claims the benefit of U.S. Provisional Application for Patent Ser. No.60/534,641, entitled NEURIC BRAIN MODELING SYSTEM IN THE MILITARYENVIRONMENT, U.S. Provisional Application for Patent Ser. No.60/534,492, entitled METHOD FOR INCLUSION OF PSYCHOLOGICAL TEMPERAMENTIN AN ELECTRONIC EMULATION OF THE HUMAN BRAIN, U.S. ProvisionalApplication for Patent Ser. No. 60/534,659, entitled NEURIC BRAIN, allfiled Jan. 6, 2004, and now expired. Application Ser. No. 11/697,721also claims the benefit of priority from U.S. Provisional ApplicationSer. No. 60/790,166, filed Apr. 7, 2006, and entitled BRAIN MODEL. Allof the above are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present invention pertains in general to artificial intelligenceand, more particularly, to emulating a human.

BACKGROUND

In certain applications, a human is required in order to assess theconditions surrounding the operation of particular system or theexecution of a certain task and to determine the progress of the task orcovered conditions in the system have changed. Once an observation ismade by a user, based upon that user's experience and information, therecan be some type of action taken. For example, it might be that a userwould make a change to the system in order to maintain the system a in aparticular operating range, this being the task of that individual.Further, it might be that the user is tasked to achieve certain resultswith the system. In this situation, the user would take certain actions,monitor the operations, i.e., the surrounding environment, and then takeadditional actions if necessary or to ensure that the task are achieved.This also the case with military operations wherein multiple individualsmight be involved in carrying out of military mission. In thissituation, multiple individuals become each having their own expertise,would be given a certain task that, when operating in concert, would beachieved tasks of achieving certain military goal. However, each ofthese individuals, although having a certain amount of specifictraining, also has the ability to make decisions that are not directlyaccorded to their experience in training. For example, if an unknownfactor entered into the mission, i.e., previously unknown obstacleoccurs, an individual has the background to make a decision, throughthis decision is made based upon prior experience in different areasthat the human might have.

Sometimes an expert system is supposed to operate a particular system totake the place of the human. These expert systems are trained based uponan expert human wherein that human is presented with certain conditionsand the actions are recorded. Whenever those conditions exist withrespect to the expert system, the expert system will take the sameactions. However, if conditions exist that were not part of the trainingdataset for the expert system, the results would be questionable, asthere is no basis upon which to make such a decision by the expertsystem.

SUMMARY OF THE INVENTION

The present invention disclosed and claimed herein comprises a methodfor emulating human cognition in electronic form. Information isreceived in the form of a textual or voice input in a natural language.This is parsed into pre-determined phrases based on a stored set oflanguage rules for the natural language. Then, the parsed phrases aredetermined as to whether they define aspects of an environment and, ifso, then creating weighting factors to the natural language that areadaptive, the created weighting factors operable to create a weighteddecision based upon the natural language. Then it is determined if theparsed phrases constitute a query and, if so, then using the weightedfactors to make a decision to the query.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and theadvantages thereof, reference is now made to the following descriptiontaken in conjunction with the accompanying Drawings in which:

FIG. 1 illustrates a diagrammatic block diagram of the overall animationsystem;

FIGS. 2 a and 2 b illustrate a diagrammatic view of an animationsequence;

FIG. 3 illustrates Influence Inclusion—An example of weighted randominfluence;

FIG. 4 illustrates Implementation of the Brain Emulation—Block diagramof brain emulation;

FIG. 5 illustrates Language Grammar Sample—Example of natural languagegrammar description;

FIG. 6 illustrates Example Parser Diagnostic Trace—Example trace ofgrammar parsing;

FIG. 7 illustrates Example Relationals Between Neurons;

FIG. 8 illustrates Organization of Neuron Tables—General organization ofneuron memory lists;

FIG. 9 illustrates Table of Neurons—Internal organization of a neuron;

FIG. 10 illustrates Example Relational Record—Contents of inter-neuronrelationship record;

FIG. 11 illustrates Event Queue and Memory—Organization of the eventprocessor;

FIG. 12 illustrates Content of an Event—General internal contents of anevent record;

FIG. 13 illustrates A Deference Table—Example table of orders ofdeference;

FIG. 14 illustrates The Layered-Temperament Personality;

FIG. 15 illustrates Characteristic Traits of the Temperaments;

FIGS. 16A-D illustrate The Four Composite Temperament Models;

FIG. 17 illustrates Typical Temperament—Weighting of Parameters;

FIG. 18 illustrates Implementation of Pressure or Trauma;

FIG. 19 illustrates Network-Connected Brain Emulation;

FIG. 20 illustrates Example Battleforce Simulation Cluster;

FIG. 21 illustrates Example Integrated Battleforce Simulation System;

FIG. 22 illustrates sample relational connections;

FIG. 23 illustrates implied relationals in linkages;

FIG. 24 illustrates the “not” relationships;

FIG. 25 illustrates a diagrammatic view of an application of the gamutto a particular mental state, directness;

FIG. 26 illustrates a diagrammatic view of a specific gamut listinterfaced with different neurons and their relational links;

FIG. 27 illustrates a detailed view of a particular neuron and the gamutassociated therewith and the interface with various other neurons;

FIG. 28 illustrates a diagrammatic view of an example of the interfacebetween two systems;

FIGS. 29 a and 29 b illustrate diagrammatic views of two types ofgamuts.

DETAILED DESCRIPTION

Referring now to FIG. 1, there is illustrated an overall diagrammaticview of the system of the present disclosure. This system is directedtoward the concept of controlling an animation engine with an animationengine 101 through the use of central animation brains 102, oneassociated with a first character named “John” and with a secondcharacter named “Jane.” Each of these brains 102, as will be describedin more detail herein below, is capable of being trained to expressemotion, that emotion being translated into control signals that can beput into the animation engine 101. A communication path 104 is providedfor communicating information from each of the brains 102 over to theanimation engine 101. This communication path can be any type ofcommunication path, such as a TCP/IP protocol. Of course, it is wellunderstood that any type of communication path can be utilized. Each ofthe brains 102, after training, will have a character, this characterbeing defined in a memory system 105 associated with each of thecharacters. In these memories is contained various experiences of theparticular character and various weights. These are trained andadaptable. During the generation of the animation, the entire animationis controlled by a director who may tweak the script. Thescreen-writer's material that is defined in a script 106 basicallyinstructs the particular brain or character to do a particular action orinstructs an input to occur in the animation engine 101. For example, aswill be described in more detail herein below, the animation engine 101can be directed to drop a box in close proximity to a particularcharacter. This essentially is in the “virtual world” of the particularcharacter. This action can then be interpreted by the brain andexperience is gained from that action through these various inputs. Aswill be described herein below, this action in the animation engine 101can elicit various emotional responses, which emotional responses are indirect response to the environmental changes within this virtual worldproximate to the character which will then cause the brain to generateoutputs to express emotion, this being facilitated by the animationengine.

Referring now to FIGS. 2 a and 2 b, there is illustrated a verysimplistic concept of this animation sequence. A character 202 isprovided in the virtual world defined as a series of vertices in the x,y and z direction. The character 202 is basically a character that canbe represented through various concepts, but has a positionalrelationship with respect to the environment. There will be a head whichcan rotate in all directions which will have perception points, thoseperception points being, for example, the eyes, the nose and the mouthand even the ears. In this example, only the concept of vision will bedescribed. Therefore, there will be two points in the virtual space thatrepresent the vision. These points can be rotated by rotating the headin an animation sequence such that they are oriented in the direction ofan object, for example, a falling box 204. The falling box isillustrated as falling from an upper level, down to a surface, and thenbouncing. As will be described herein below, the character 202 isanimated to recognize the box, move its head to view the box and followthe box to the upper location to the lower location and as it bounces.Further, as will be described herein below, there will be emotionexpressed as a result of seeing the box and any actions that may occurwith respect to the box in the environment of the individual. FIG. 2 billustrates a situation wherein the individual is aware of the box inthe personal environment and in proximity thereto. And, after seeing thebox, viewing the box as a threat. Once the box is viewed as a threat, itcan be seen that the character, in the lower portion of FIG. 2 b, isplaced into an animation sequence wherein the character will evade thebox and move away from the box to a potentially safe area. As noted,this will be described in more detail herein below.

Core Brain

The central brain of the present disclosure distills the temperament,personality and instantaneous state of a human individual into a seriesof Brain Parameters. Each of these has a value varying from zero to 100percent, and is loosely equivalent to a single neuron. These parameterscollectively define the state of the person's being and specify mattersof temperament and personality. Some parameters are fixed and seldom ifever change, while others change dynamically with present conditions.

Relationships between parameters, if any, are pre-established. TheParameters are connected with the rest of the brain model in such amanner as to alter the decision processes, decision thresholds and theimplied personal interests of the underlying model they become a partof.

The exact list of Parameters and their definitions are not germane tothe system of the present disclosure, and may include more or fewerparameters in any given implementation thereof. Numerous parametersdefine certain tendencies specific to certain temperaments. Some definethe present emotional state, such as sense of confidence in a decision.Others are place-holders that define such things as the present topic ofconversation or who the first, second or third persons in theconversation are. Yet others define physical parameters such asorientation within the environment, sense of direction, timing and thelike.

Some brain Parameters may be loosely arranged in a hierarchical fashion,while others are not, such that altering any one parameter may affectothers lower in the hierarchy. This arrangement simplifies theimplementation of personality.

Example Parameters. Table 1 illustrates a few of several hundred suchparameters by the way of example. The ‘Choleric’ parameter, 202 forexample, is ‘above’ others in the hierarchy, in that altering thepercentage of Choleric temperament affects the value of many otherparameters. For example, it affects the Propensity to Decide 222. Eachcan be treated as a neuron that may be interconnected with other(non-parameter) neurons. The parameter neurons may serve in a rolesimilar to an I/O port in a digital computer.

The below table is not a complete set of parameters, but is arepresentative set of parameters useful for the explanations thatfollow.

TABLE 1 General Examples of Brain Parameters Parameter Description 201Root Temperament Choleric, Melancholy, Sanguine or Phlegmatic 202Choleric, Ratiometric Percentage contribution of Choleric attributes 203Melancholy, Ratiometric Percentage contribution of Melancholy attributes204 Sanguine, Ratiometric Percentage contribution of Sanguine attributes204 Phlegmatic, Ratiometric Percentage Contribution of Phlegmaticattributes 209 Gender Male or female Sense of Confidence (Decisions)Degree of confidence in a decision Sense of Confidence (Motor Skills)Degree of confidence in present motor skill Sense of DeterminationDegree of determination to continue present plan Sense of Dread Presentsense of dread being experienced Sense of Enjoyment Present sense ofenjoyment Sense of Embarrassment Present sense of embarrassment 229Present need to Defer Present need to defer to external person's desire230 Trauma State of physical or emotional trauma Present Goal (1 of n)Present objective(s), a list Long Term Goal (1 of n) Long termobjective(s), a list Topic of conversation (1 of n) The present subjectof conversation, a list Self Identify Recognition of identity such astarget for communications Present Speaker Identity of person speakingPerson Spoken To Identity of person being spoken to Present ObjectIdentity of object/person being spoken of 235 Correlating Facts, statusTrue of presently correlating information 236 Hottest Node, statusHottest-firing node in context pool, for threshold scaling 237 ActivityThreshold Minimum firing level for context pool memory

In traditional models of the human brain, facts are simplisticallyrepresented as a single neuron, each of which may ‘fire’ at some levelof 0 . . . 100%. The degree of firing is construed as an indication ofthe present recognition of that fact. These neurons are interconnectedby weighted links, based upon the relationship and experience betweenconnected neurons.

Example Decision-Related State Parameters. Some of the key stateparameters used in the decision process are detailed below. Some are setby personality traits, some by the context of the moment and aredescribed elsewhere. Several have baseline values established by thePropensity to parameters.

Activity Threshold 237 is the minimum percentage of full-scale that aneuron must fire before it is considered a candidate for inclusion inshort-term memory.

Base Decision Threshold 250 is a personality-based starting basis forthe decision threshold. Long-term training and learning experience canraise or lower the base value.

Correlating Facts 235 is true if the correlator portion of the analyzeris presently correlating facts, usually in support of an analyzerdecision.

Hottest Node 236 points to the hottest-firing neuron in the context pool(short-term memory). The analyzer uses it for scaling decisionthresholds.

Importance for Action 215 is the relative importance of making adecision. It is initially based on the propensity for importance ofaction, and can be scaled up down by the analyzer as the result ofrecent decisions.

Need for Completeness 260 indicates the relative need for complete (andquality) facts, prior to making a decision. Incomplete facts will causethe Conjector to make suitable guesses, but the resulting ‘facts’ willbe of lower quality.

Urgency for Action 216 represents the urgency (not the importance) ofmaking a decision. Higher levels of urgency make lower quality ofinformation (and decisions) acceptable.

Example Temperament-Based Propensity Parameters. A typical set of basicbrain Parameters which indicate various propensities based upontemperament are given in Table 2, including representative contributionratios (given as a percentage). This set of values is by no meanscomplete and is given for the sake of description of the mechanisms ofthis disclosure. Other Temperament Parameters may be identified andincluded in this list, without altering the methods and claims of thispatent.

The specific percentages given in Table 2 are representative and typicalvalues used, but are subject to ‘tweaking’ to improve the accuracy ofthe psychological model. Other values may be used in the actualimplementation. Further, the list is representative and is not complete,but serves to demonstrate the system of the present disclosure.

It has been observed (and incorporated into Table 2) that, generally,many of these parameters reflect traits shared primarily by two of thetemperaments, with one of the two being greater. That same parameter mayalso be shared minimally by the remaining two temperaments.

TABLE 2 Examples of Temperament Parameters Parameter Choleric MelancholySanguine Phlegmatic 210 Propensity for Amusement 10 35 35 20 211Propensity for Completeness 20 35 10 35 212 Propensity for Determination35 20 10 35 213 Propensity for Enjoyment 10 25 40 25 214 Propensity forFun 10 20 55 15 215 Propensity for Importance of 50 10 35 5 Action 216Propensity for Urgency of 35 12 50 3 Action 217 Propensity for Patience15 35 5 45 218 Propensity for Rhythm 10 15 60 15 Influence 219Propensity for Stability 10 25 5 60 220 Propensity to Analyze 10 60 5 25221 Propensity to Care-Take 5 10 30 55 222 Propensity to Decide Quickly50 15 30 5 223 Propensity to Follow a Plan 10 25 5 60 224 Propensity toPlan 50 35 10 5 225 Propensity to Procrastinate 5 15 30 50 226Propensity to Second-Guess 5 60 10 25 227 Propensity for Stability of 1025 5 60 Action 228 Propensity to Rest Hands on 25 60 5 10 Hips or inPockets

The system of the present disclosure presumes the use of a node thatdefines the desired underlying temperament, and additional nodes thatdefine the desired percentages of the four temperaments. Table 2 is achart of the selected typical tendencies for each of the temperaments,with each numeric value giving the approximate likelihood of the giventrait to be demonstrated by the four temperaments, as a percentage.

The percentages given are by way of example, although they mayapproximate realistic values. The altering of these values by no meansalters the means and methods of this patent, and they may be adjusted tobetter approximate temperament traits. The list is by no means completeand is given as a set of representative parameters for sake of example.

In many, but not all, cases, the overall impact of a temperament isgiven by the product of the temperament's percentage, as pre-selected toproduce the desired personality, and the percentage of likelihood givenfor each propensity from Table 2. This is demonstrated in FIGS. 4 and 5.These may be augmented by additional variations due to the Gender 201parameter, accounting for differences in response by male or femalegender.

Detail of Some Temperament-Based Propensity Parameters. The samplings ofparameters in Table 2 are described below, by way of example of how suchparameters are specified and applied. The described settings andapplications of these parameters are necessarily subjective, and therelative weightings of these and all other parameters described in thisdocument are approximate and exemplary. One skilled in the art willrealize that they may be altered or adjusted without altering the meansof the system of the present disclosure.

The Propensity for Amusement 210 is the tendency to be amused. Thehigher values lower the threshold of what is found to be amusing,triggering amusement sooner. The triggering of amusement may bereflected in the appropriate facial expressions, as provided for in theunderlying brain model and skeletal mechanics, if any.

The Propensity for Completeness 211 is a measure of the personality'stendency to need complete facts before making a decision, and is basedsolely on temperament selection. It is naturally highest for theMelancholy and naturally lowest for the Sanguine or Choleric. While itis normally not altered, the underlying brain model (analyzer) can raiseor lower this parameter based upon training or learning.

The Propensity for Determination 212 is the tendency for the brainemulation to be determined, and sets the baseline value for the sense ofdetermination. Over time, it can be permanently altered by achievement(or failure to achieve) targets or goals.

The Propensity for Patience 217 is a measure of the overall tendency forpatience. The level is normally high for a Phlegmatic and low for aSanguine, but is also significantly affected by (long term) experiencehistory. Growth in this trait parameter is very slow and is an iterativeprocess. High levels of Patience 217 can suppress early termination ofaction, when faced with repeated failure to meet short- or long-termgoals.

The Propensity for Fun 214 defines the tendency of the temperament tomake decisions based on the sense of feel-good. It is temperamentdependent, tends to be highest for the Sanguine, and heavily influencesthe impact of Rhythm Influence.

The Propensity for Importance of Action 215 is a measure of thetemperament's tendency to find action important, whether or not all thefacts needed for decision are available and with high confidence. It isnaturally highest for the Choleric and naturally lowest for theMelancholy and Phlegmatic. While it is normally not altered, theunderlying brain emulation can raise or lower this parameter based upontraining or learning.

The Propensity for Urgency of Action 216 is a measure of thepersonality's tendency to find action important, at the expense ofstrong consideration or analysis of the facts. It is naturally highestfor the Sanguine and naturally lowest for the Phlegmatic. While it isnormally not altered, the underlying brain emulation can raise or lowerthis parameter based upon training or learning.

The Propensity for Patience 217 is a measure of the overall tendency forpatience. The level is normally high for a Phlegmatic and low for aSanguine, but is also significantly affected by (long term) experiencehistory. Growth is in this trait parameter is very slow and is aniterative process. High levels of Patience 217 cause suppress earlytermination of action, when faced with repeated failure to meet short-or long-term goals.

The Propensity for Rhythm Influence 28 is a temperament-dependentparameter, and may be altered up- or downward by hyperactivity. Itcontrols the relative effect of rhythm on the decision process. Itsbaseline value is relatively higher for the Sanguine.

The Propensity for Stability 219 is a temperament-dependent parameterthat defines the tendency towards stability. When the value is high,decisions will tend to be made that lead to no net change, in the senseof foot-dragging. It also implies a tendency to procrastinate, and is astrength (or weakness) of the Phlegmatic personality. High levels ofStability 219 lead to strong loyalty towards the context-dependentauthority.

The Propensity to Analyze 220 is determined by temperament and is notaffected by other properties, except by external command. Even then, itseffect is short term and rapidly trends back to the base tendency. Whenvery high, there is a marked tendency to analyze and correlate factsbefore making decisions, and the confidence-based decision thresholdsbased on the outcome are normally raised.

The Propensity to Care-Take 221 is a temperament-dependent parameter,tending highest in the Phlegmatic and Sanguine. It increases theinterest in acquiring people-related facts for short-term memory. Theimpact of this parameter is established, for example, by altering theparameters of the Clutter Filter for the context pool or short termmemory.

The Propensity to Decide 222 is a parameter that is highest for theCholeric and Sanguine temperaments, and influences (increases) thewillingness to make decisions with a minimum of facts. For the Choleric,decisions subsequently proven inferior may be altered, while for theSanguine, the results tend to be ignored. Parameter 222 also increasesthe tendency to revise decisions as higher-quality facts are available,and decreases the stability in decisions and the tendency to foot-drag.

The Propensity to Follow the Plan 223 is the (current) level of tendencyto follow a plan. Its core value comes from personality traits, but isaltered by such variables as stress, urgency, and external pressure.When pressure is high, as per Trauma parameter 230, there is increasedtendency to ignore the plan and to revert to personality profile-basedresponses. This is accomplished in a manner such as demonstrated, forexample, in FIG. 5

The Propensity to Plan 224 is a measure of the tendency and desire towork out a plan prior to a project or task, and is a function of thetemperament profile. If Propensity 34 is high, work on the task will besuspended until a plan of steps in the task is worked out. Thepropensity to plan does not imply a propensity to follow the plan, per223.

The Propensity to Procrastinate 225 is a measure of the tendency toprocrastinate, deferring decisions and action. The primary value derivesfrom the temperament per Table 2, and is then a fixed parameter butwhich may be gradually altered by experience or training. Whileprocrastination is largely a characteristic of the Phlegmatic, it alsooccurs in the Melancholy decision-making process, in the absence ofcomplete facts, and is normally very low for the Choleric.

The Propensity to Second-Guess 226 is a measure of the tendency toreevaluate decisions, even quality decisions, and possibly to evaluatethem yet again. Temperament-dependent as shown in Table 2, it is highestin the Melancholy and typically lowest in the Choleric.

The Propensity to Stability of Action 227 is a measure of the tendencyto maintain the status quo. Largely a Phlegmatic trait, it influences(increases) the tendency to foot-drag, and is implemented by a decreasedwillingness to alter plans. It may be connected to the underlying brainemulation or model as a part of the clutter or interest filter at theinput of the context pool, short term memory or analyzer, suppressingnew plans or suggestions that abort existing or active plans.

Propensity to Rest Hands on Hips 228 is a largely Melancholy trait whosemore positive values increases the tendency of any attached mechanicalskeleton to find a resting place for its hands, primarily on the hips orin the pockets. This parameter provides a control value to theunderlying brain emulation or model, which itself is responsible for themotor skill issues that carry out this tendency. That emulation or modelactually determines whether or not this tendency is carried out.

Again, parameters in Table 2 are directly controlled by one or more ofthe four underlying temperament selection parameters. They are scaled bypercentages such as those also given by example in Table 2. They arethen distributed by the brain model to the appropriate control points,filters and selectors within the underlying brain emulation or model.

Inclusion of Parameter Influence. Throughout the brain emulation, thereare many places at which a parameter may or may-not influence theoutcome of a decision. The likelihood of the parameter contributing tothe decision in some cases are often statistically based. One method ofaccomplishing this is shown in FIG. 3. A random number between 0 and100% is generated by 421 and is compared by 422 against the parameter inquestion. If the parameter value exceeds the sum of a base thresholdparameter 423 and a random number, inclusion is enabled.

This type of logic is frequently used in the clutter filter discussedelsewhere.

Derived Brain Parameters. Many parameters derive from the basicTemperament Parameters of Table 2. These values may be a combination oftemperament parameters, but as adjusted for learning, training,experience and present conditions. As with other brain nodes andparameters, most of these are expressed in a range of 0 . . . 100%, inunits suitable to the technology of implementation.

A typical set of these derived parameters is given in Table 3. Each ofthese has an additional (signed) value to be added to it which isfurther adjusted on the basis of learning or training. The list is by nomeans complete, and is given for the sake of description of themechanisms of this disclosure. Many of these relate to matters ofemotion, its measure and expression. These, as may all parameters, maybe monitored externally to measure the emotional state of the emulatedbrain.

TABLE 3 Examples of Derived Brain Parameters Decay Targets DerivedParameter Choleric Melancholy Sanguine Phlegmatic 250 Base DecisionThreshold 10 45 5 40 251 Concentration Ability 10 60 5 25 252 Docility 525 10 60 253 Hyperactivity 25 10 60 5 255 Filter Organizational Detail 525 10 60 256 Filter People Interest 60 25 5 10 258 Filter RelationalDetail 10 60 5 25 259 Filter Technical Detail 45 5 40 10 260 Need forCompleteness 10 40 5 45 261 Patience With Detail 5 60 10 25 262Procrastination Level 5 25 10 60

These parameters may be derived from temperament, context, environmentaland current-condition parameters, for example, although other means willbecome obvious during this discussion. The parameters of Table 3 areexemplary. Most parameters in this table decay over time to the valuesshown at the right. These decay targets are nominal and may be alteredthrough preemptive training. They derive from temperament percentages ina similar manner to Table 2. The list is by no means exhaustive orcomplete, and others will also become obvious during this discussion.

The current derived parameter values are distributed to the appropriatecontrol points, filters and selectors within the brain emulation ormodel. In some cases, they control decision or stability thresholds, orestablish the statistical settings, such as per 42 of FIG. 3, forcurrent-interest filters in the emulated brain, and to other such brainemulation functions. The composite impact of these temperament andtemperament-derived parameters determines the composite personality ofthe emulated brain.

The Base Decision Threshold parameter 250 is the starting basis for manydecisions. It is the typical starting decision threshold, and is ameasure of confidence or information completeness that must be obtainedbefore a decision will be made. The threshold is given as a percentage,0 . . . 100%, whose application depends upon the types of decisionsbeing made. In some places it is used as an absolute threshold, or mayspecify a figure of confidence in the present facts, a figure that mustbe exceeded before a decision may be made.

The Concentration Ability parameter 251 is a measure of the ability toconcentrate. A more positive value raises the threshold of attention tooutside distractions, those unrelated to the issues in short term (orcurrent context) memory in the underlying brain model or emulation. Itis used by both the analyzer 30 and the clutter filter 40.

Docility 252 is a measure of the overall propensity for stability duringexternal emotional pressure. It contains a long-term filter that decaysback to the base value. Positive Docility 252 greatly increases thethreshold of attention to emotional trigger events. Docility 252 can bealtered over moderate periods of time, but tends to return to itstemperament-defined static value. When this value falls lower than itsaverage setting, there is an increasing tendency to ignore learnedresponses and to revert to personality profile-based responses.

Hyperactivity 253 is a measure of current levels of hyperactivity, aswould be normally defined by someone skilled in the art. It isestablished by a programmable value and subsequently augmented bytemperament percentages. Hyperactivity is also influenced by Docility252 and current emotional stress. These sources are the primarydeterminers for the base value of hyperactivity, but long-term trainingor experience can alter the value. Choleric and Sanguine temperamentshave relatively higher values, while Melancholy and Phlegmatic valuesare quite low.

The impact of Hyperactivity 253 is implemented, for example, byintroduction of (typically negative) random variations in the magnitudeof selected decision thresholds. It also alters the time constants oftask-step performance and present rhythm parameters, with additionalultimate impact upon the performance of motor tasks.

Filter Organizational Detail 255 specifies the filtering oforganizational detail from incoming information, context pool orshort-term memory for the brain emulation. A value below 100% removesthe greatest percentage of detail.

Filter Human Interest 256 specifies the filtering of human-interest datafrom the incoming information, context pool or short-term memory in theemulated brain. 100% removes most human-interest information. The valuewill be highest for Choleric models and lowest for Sanguinetemperaments.

Filter Relational Detail 258 specifies the filtering of detail aboutinter-relationships between facts from the incoming information, contextpool or short-term memory. 100% removes most detail. The value ishighest for Phlegmatic and Sanguine models and lowest for the Melancholymodels. Higher levels inhibit the correlation of distant facts that arenonetheless related. Lower levels also encourage the analyzer 30 tospawn events to event memory 14. This has the effect of iterativelyrevisiting the same information to analyze short-term memory for bettercorrelation of data.

Filter Technical Detail 259 specifies the filtering of technical detailfrom the incoming information, context pool or short-term memory for thebrain emulation. 100% removes most detail. The value is highest forCholeric and Sanguine models, and lowest for Melancholy models.

The Need for Completeness parameter 260 establishes the required levelof completeness of information before making a decision. A higher valueof completeness increases the likelihood of deferring a decision untilall the facts are available, sometimes stymieing or stalling a decision.Other parameters related to importance and urgency can alter thisparameter. The need for completeness can be altered by a decision of theanalyzer 30, and upon external command to the brain emulation, such asthrough 93.

As the context pool (short-term memory) shrinks over time because ofrest, the need 260 drifts backwards to the value set by the propensityfor completeness. The need also reverts to the propensity value after adecision has been made. 100% implies the highest need for completeness.It is highest for Melancholy and lowest for Choleric and Sanguinemodels.

Patience With Detail 261 is the present level of patience. Its baselinevalue derives from the propensity for patience. It is affected bypresent conditions and can be commanded to rise. It largely altersdecision thresholds, and values near 100% imply comfort with detail. Thevalue is dynamic and tends highest for the Melancholy and lowest forSanguine and Choleric.

Procrastination Level 262 is a measure of the present level ofprocrastination. Its base value is set by the propensity toprocrastinate, is increased by uncertainty, and decreased by impatience.Procrastination defers decisions and postpones actions that are nototherwise inhibited by circumstances. Decision choices are implementedin a manner similar to 42 of FIG. 3. Higher values of this levelpostpone decisions, even in the presence of hard facts (high sense ofcertainty).

While procrastination is largely a characteristic of the Phlegmatic, italso occurs in the Melancholy decision-making process in the absence ofcomplete facts. It is normally very low for the Choleric.

As noted, the parameters described in the preceding tables in no wayconstitute a complete set of obvious ones, which total in the hundreds.Selected parameters have been presented by way of illustrating theinternal processes and considerations for the brain emulation of thepresent disclosure.

Implementation of the Brain Emulation. One implementation of theunderlying functional model of the brain is diagrammed in FIG. 4. Threeprimary elements of the model are analyzer/correlator 30, the contextpool memory 10, and the English semantic analyzer 50.

Throughout the descriptions, English is always used where the processingof external communications are involved, whether in complete sentencesor in sentence fragments. Internally, the system is essentially languageindependent, except where linguistics, phonics, the spelling of words orthe shape of letters used in the language are involved. For ease ofinitial implementation, English was used, but essentially identicalprocesses can be applied to any human language of choice. The choice oflanguage in no way limits the disclosure for purposes of this patent.Indeed, the methods of this patent can be applied to autonomouslytranslate one human language to another.

Referring to FIG. 4, various elements are controlled or modified by thestate parameters previously discussed. In particular, the Clutter Filter40 plays a central role in determining what types of information areactually considered in the brain. As are most other blocks in thefigure, operation of the analyzer/correlator 30 is controlled or heavilyinfluenced by personality state parameters 22. These same parameters maythemselves by the results of analyzer 30, in many cases.

The flow of external information enters through the semantic analyzer50. This distills content and intent from both English sentences andsentence fragments, and formats the distillate for inclusion intoshort-term memory 10.

Concept of the Neuron Used Here. This disclosure makes no attempt toreplicate the biological neuron, axion and dendron, their arrangement orinterconnections, or their redundancy. Rather, the term neuron in thispatent describes the means to remember a single fact or experience. Assuggested bio-mimetically, the existence of a single fact is representedsimplistically by a single neuron, while the implications of that factare contained in the arrangement of interconnects between neurons.

In the biological neuron, there is an in-place ‘firing’ of a neuron whenthe associated fact is recognized. When, for example in a fox's brain, aspecific neuron represents a common rabbit, the firing of a biologicalneuron implies recognition of that rabbit. The degree of firing (oroutput) represents the degree of certainty with which the rabbit isrecognized.

There is no such equivalent in-place firing of the neuron in theemulation or brain model of this disclosure. In a digitalimplementation, the entire long-term memory 12 (where facts,relationships and experiences are stored) could be composed of read-onlyor slow flash memory, because recognition does not involve a change ofthe neuron's state in that memory.

As an alternative process used here, recognition takes place by theexistence, recognition or correlation of data within the context poolmemory 10. Any reference to a ‘firing neuro’ is to be construed asplacement of a reference to (address-of) that neuron into context pool10, along with a current firing level for it.

Neurons and Reference Indices. Every neuron records two types ofinformation. The existence of a specific fact is implied by the factthat a neuron to represent that was defined at all. Experiences areimplied by the relationships and linkages formed between neurons.Individual neurons are emulated by some fixed-size base information, anda variable number of relational connection records, as shown in FIG. 9.Relational conditions may be conditional, predicated upon the state ofother neurons, and reference the ID indices of both their target neuronsand condition triggers.

All neurons have a unique address, but it may be changed from time totime as memory is reorganized. Further, the very existence of someneurons is tentative. They may disappear unless reinforced over a periodof time, and are located in the reinforcement memory 11. Because theirprecise locations are unstable, references of one neuron by anothercould be problematic. Further, the relative size of a neuron can varywidely, depending upon the inter-relationships and context with otherneurons.

To handle these matters gracefully, a unique and unchanging index isallocated for each neuron created. References between neurons use thispermanent index to inter-reference each other. If a neuron is deleted(in reinforcement memory 11), the index is reclaimed for later reuse. Aspecific bit within the index value indicates whether it refers to anormal permanent neuron or to the reinforcement memory 11. A fixedsubset of the indices to the reinforcement memory ‘tentative’ neuronsare also reserved and used to indicate information block type and formatwithin the context pool 10.

Neurons in the reinforcement memory 11 that have been reinforced over aperiod of time are made permanent by the analyzer/correlator 30. Theanalyzer then moves them to permanent memory 12 and alters allreferences to its index to show that it has been so moved. Referenceswithin that neuron may themselves not survive the reinforcement process,and may be deleted during the transfer. Refer to Table 4 for data storedwith the individual neuron.

Content of Neural Reference Structures. The analyzer/correlator 30repeatedly scans context pool memory 10 for both unprocessed informationand for activities suspended while awaiting occurrence of certain eventsor conditions. It also updates brain parameters both to keep themcurrent and to check for relevant changes of substance.

Within the context pool, information is organized into variable-sizedblocks, with all of it pre-classified or typed prior to submission. Someblocks contain inferred intent from sentences. Others contain commands,propositions, conjecture and other miscellaneous material. In itsdegenerate form, a ‘block’ may simply be a reference to a single neuron,and its firing level.

TABLE 4 Neuron Structural Content Neural Content Description BasicInformation Basic information may include references to explicitspellings (e.g., a walk-back index to the text-tree for the word),pronunciation exceptions, visual-object descriptors and the like.Certain flags and start-indices for lexical matters and the like arealso included here. Relational Linkages The weighted and conditionalinfluence of this neuron upon another is defined by relational linkages,of which there may be up to 1000 or more, for some neurons. Each newexperience and relationship learned has a relational linkage created forit. Initially, these relationships are created in the reinforcementmemory, where they remain until later validated and moved to long-termmemory (or are deleted). Relationals in reinforcement memory may referto neurons in either memory, but those in long-term memory may referonly to other neurons in long-term memory. The Analyzer tracks theallocation, aging, validation, and ‘garbage- collection’ processes, andthese are discussed in detail elsewhere.

Individual neurons are emulated by some fixed-size base information, anda variable number of relational connection records. The latter may beconditional, predicated upon the state of other neurons, and referencethe ID indices of both their target and conditional neurons.

Context Pool Memory 10. The core of all emulation occurs in the contextpool (short term) memory 10 and the analyzer/correlator 30. Allinformation of immediate awareness to the emulator resides in thatcontext pool 10. Neuron-like firing is implied by the very existencewithin the context pool 10 of a reference to a neuron from long-termmemory 12. Information (blocks) enter the context pool 10 serially, asit were, but are processed in parallel by the analyzer 30.

Referring the context pool 10 in FIG. 4, data flows from right to left,as it were. Unless reinforced, all neuron data in the pool gradually‘leaks away’ or dies away during its travel, aging it. Should thecontext pool fill, oldest (or left-most) data is simply lost, a case ofinformation overload. Any data remaining in the context pool 10 that hasaged without reinforcement can eventually decay to a zero-firing state,at which point it is simply removed from the pool.

Data may be placed into the context pool 10 from a number of sources,the initial one of which is often the semantic analyzer 50. Except forinputs from the analyzer 30, all context pool information is filtered bya clutter filter 40, which largely keeps irrelevant or non-interestingdata from reaching the context pool 10.

Data in the context pool take the of form block-like structures ofpredefined format. A block arriving from the semantic analyzer 50, forexample, contains the intent of a sentence, independent clause orsentence fragment. A one-word reply to a question is fully meaningful assuch a fragment. Such a sentence block may contain references to aspeaker, the person spoken to, and possibly, references to the person orobject discussed. Many combinations of this and other sentence data arepossible.

Blocks from analyzer 50 frequently include the purpose of the sentence,such as query (and expected type of answer), command, factualdeclarations, observations and the like. This type of data is discreteand readily identifiable by the semantic parse.

Other implied emotional information may be inferred from use ofsuperlatives, exclamatories, and tone (if derived from an auditoryanalyzer 60). Auditory sources yield the speaker's nominal fundamentalfrequency and infer stress or emotional excitement by short or long-termpitch deviations accompanying spoken speech.

The length of the context pool is determined empirically by theapplication, but is nominally sufficient to handle a number of hours ofintense study, or approximately a day of casual interaction. To putsizes into context, this represents roughly a megabyte of conventionaldigital storage, although selected size does not alter the means ormethods of this patent.

During sleep times (or emulated extended rest), the context pool 10gradually drains, with neural firings gradually fading to zero. Asneural references fade to zero, they are removed from the context pool,as suggested bio-mimetically.

New information may be introduced during sleep by the dreamer block 75.Dreamer-derived information created during deep sleep decays rapidlywhen awake, at rates different from normal context pool data decay. Ifthe sleep time is insufficient, yet-active neural firings remain intothe following wake cycle; these are handled as previously described.

Language Syntax Analyzer 50. A language semantic analyzer 50 acceptscommunications in the natural language of implementation, English, forexample. It breaks down sentences, clauses, and phrases to derive intentand purpose from the sentence. It uses the context of the currentconversation or interaction by polling the analyzer 30, long-term memory12 and reinforcement memory 11. Access to present context is obtainedindirectly from the context pool via analyzer 30. Interpretation oflanguage words is weighted by the presence of their associated neuronsin the context pool, yielding context-accurate interpretations.

While language semantic analyzer 50 could be hard-coded in logic, it isbeneficial for many applications that it be implemented as an embeddedprocessor. This method is not required for the purposes of thisdisclosure, but is a convenience for the parse and interpretation oflanguages other than the initial design language.

Because all humans are essentially the same regardless of their nationallanguage and its grammar or semantics, the parameters described hereinremain constant, while language semantic analyzer 50 languagedescription script would change.

For convenience, statements emitted by analyzer 30 through interface 98are created in analyzer 30. However, this function could be separatedinto a separate unit for convenience in altering the language of choicefrom English.

For a given language, semantic analyzer 50 recognizes a set of wordsthat are an essentially invariant part of the language, such as with andfor, in English. These play a substantial role in defining the grammarfor the language. Nouns, verbs and adjectives readily change with theages, but the fundamental structural words that make up the underlyinggrammar rarely do.

In addition to these invariant ‘grammar’ words, the structure ofsentences, clauses and phrases define the remainder of the grammar.Analyzer 50 uses this overall grammar to interpret the intent of thecommunications.

Computer languages (non-natural languages) are often parsed by separatelexical and grammar parsers, using such commercial tools as Lex andYacc. These were deemed burdensome and unwieldy for parses within thesystem of the present disclosure. For natural languages, an alternativeparser (Lingua, a commercial parser and not the subject of thisdisclosure) was created. Using Lingua, a highly complete description ofEnglish grammar was defined and serves as the basis for languagesemantic analyzer 50. The intellectual property contained therein is adefinition of English grammar itself, although it is also not thesubject of this disclosure.

In the prior art, custom analyzers using large corpuses or dictionariesof words have also been employed for the parsing of English text. Unlikethem, semantic analyzer 50 makes use of context-dependent informationfor a more accurate rendering of intent from the text.

Semantic analyzer 50 takes in natural language sentences, clauses,phrases and words, and emits blocks of decoded neuron references andinferred intent. In large measure, the non-changing and fundamentalgrammar words are discarded after they have served their purpose in theparsing. Similarly, structural constructs within sentences are oftendiscarded after their implications have been gleaned. Finally, pronounreferences such as he and it are replaced by references to neuronsrepresenting the resolution targets, such as “David Hempstead” or“rabbit”.

The semantic analyzer indirectly references both long term 12 and the“21-day” reinforcement memory 11, and can extract relational informationfrom either, to determine meaning and intent of specific words. Itplaces greater weight on words whose neural references are alreadyfiring within the context pool 10.

The definitions of English (or other natural language) grammar arecontained in a definition file in a variant of the Baccus-Nauer Format(BNF). Refer to FIG. 5 for an example fragment of such a definition. Theexample was implemented using the Lingua compiler, a commercial productof Neuric Technologies. An example of diagnostic results obtained fromparsing the sentence, “The table failed.” is given in FIG. 6, showingthe iterative nature of the parser used in the commercial Linguaproduct.

It can readily be seen by one skilled in the art that the languageanalyzer 50 can be implemented variously without detracting from itsplacement and efficacy in the system of the present disclosure.

Sentence Blocks. For sentence processing, context pool 10 data may beblocked into inferred facts and data. Preprocessing in semantic analyzer50 will have already converted sentence fragments into completesentences, or will have flagged the fragments for expansion by theConjector.

Each sentence block is usually a complete sentence, with subject andpredicate. Implied you subjects have had the subject resolved andappropriate neuron reference substituted. The implied It is prefix, thatturns a noun-clause (e.g., an answer to a question) into a fullsentence, would also have been added as needed. All sentence blocks arestandardized in form, with inferred sentence information reordered intothat form.

The blocks are of variable length, with flags that indicate the sentencedata being stored. Some of this information is gleaned from stateparameters. The sentence type dictates which items are optional. Typesinclude Declaration, Question, Exclamation, Observation, Accusation,Answer to Query, and yet others. Other sentence data may include thefollowing (and other) information:

Subject

Subject Person: (1st, 2nd or 3rd)

Subject Count: (Singular, Plural)

Subject Gender: (Male, Female, Object)

Action or Step to Take

Verb

Object (including Person, Count, Gender)

Target of Action (including Person, Count, Gender)

All noun-like items also contain the person, count, and gender flags.These sentence blocks are interpreted by the analyzer/correlator 30 andthe conjector 70 as commands for interpretation. Some of these aredescribed in the discussion about Table 7 contents.

The Sentence Recognition Process. Regardless of whether the sentence wasobtained through written text or from auditory speech, recognition andunderstanding of sentence content is roughly the same. The greatestdifferences are the additional cross-checks, validations, and filtersimposed on spoken speech. For extracting intent from sentences, ageneral communications triad is defined: The speaker, the person/objectspoken to (e.g., the receiver of commands), and the person, object orsubject spoken of. Most of this information can be inferred fromsentence content, from the present context pool 10, and from stateparameters 20 and 23.

The basic process is:

1. Parse—Parse the sentence using language grammar rules, such as inFIG. 5.

2. Extract the Triad Corners—Identify shifts in the communicationstriad, if any. For identified shifts, advise correlator 30 by suitablecommand notifier in the context pool 10.

3. Extract any Qualifiers—Compile qualifier clauses. If a definitivesentence, store the compilation, but otherwise evaluate the clause'sprobability to a single neuron, extracting both neuron references anddata sufficient to create additional relational connections 1252.

4. Extract Structural Elements Extract key structural elements,discarding semantic information. Store the data in appropriate blocks orneuron references for use by the correlators 30 and 75.

5. Compile Definitives—Compile any definitive sentences into relationaland qualifier constituents, storing the relational associations (if any)with the relevant fact neurons. This is done indirectly by submitting anappropriate directive to the context pool 10.

The above basic process is exemplary of a portion of the typicalactivity for parsing a sentence and generating information or commandblocks for inclusion in the context pool 10.

Clutter Filter 40. Clutter filter 40 acts to limit entry of certaintypes of information into context pool 10. Information entering thecontext pool 10 must pass through the clutter filter 40, except for thatemitted by analyzer 30. The purpose of the filter 40 is to removeextraneous neurons, such as language or grammatical tokens andnon-significant gesture information. The clutter filter 40 followspreset heuristics which may either be fixed or adaptable.

The result of the filter is to maximize the consideration of relevantinformation and to minimize ‘mental clutter’ and things of littleinterest to the personality being modeled. Choleric temperaments, forexample, do not thrive on human-interest information as the Sanguinedoes. Data so identified may be removed in keeping with currentparameter conditions. This may occur during the course of conversationalexchange, during which time semantic analyzer 50 or other sources flagsthe data on the basis of the topic of discussion.

The clutter filter is a substantial contributor to the emulationdifferences in right-brained and left-brained activity, second in thisonly to the work of analyzer/correlator 30.

During interaction with the outside world, a large number of neurons arereferenced from memory and submitted to the context pool 10 foranalysis, correlation, conjecture and dreaming. The filter considers thetype and groupings of neurons being submitted, as well as some of theinhibitor factors, and may opt to discard them instead forwarding themto the context pool 10. During normal (non-sleep) activity, outputs fromthe dreamer 75 are given very low priority, unless overall levels ofneural firings in the context pool 10 are very low.

Neural phrase results from the analyzer 30 always enter short-termmemory directly, bypassing the clutter filter 40. By the nature,analyzer/correlator 30 governs overall thought (and memory) processesand normally does not produce clutter.

The filter also prioritizes incoming information. Entire contents ofanswers to questions are also likely to be passed through, whereas thesame material might not ordinarily be.

The primary basis of determination of what constitutes ‘clutter’ is thepersonality parameters 20, a subset of the state parameters 22. (In FIG.4, they are shown separately from other parameters for emphasis andclarity, but are essentially are the same.) Logic such as that of FIG. 3demonstrates one means by which the clutter determination may be made.It will be obvious to one skilled in the art that the clutter filter 40as described here can be augmented with additional rules and heuristicswithout altering the basic disclosures of this patent.

Analyzer/Correlator 30 The analyzer/correlator 30 is the heart of theemulated brain, and is the primary center of activity for thoughtprocesses. It is also the primary means for updating of all dynamicbrain parameters and is the only means for initiating permanent storageof information.

Decisions are normally based upon ‘solid’ facts, information of highconfidence or firings. Generally speaking, higher perceived quality ofthe source information yields higher quality decisions. In the absenceof good information, analyzer 30 uses information from conjector 70,although results using the latter are also of lower quality.

Thought and decision processes are performed by the analyzer block, withsupporting prompts and suggestions from conjector 70 and dreamer 75blocks. The heart of the analyzer's work is done in context pool memory10, such that all processes are performed within the context of themoment.

Entry of a neuron reference into the context pool memory 10 initiates asequence of events unique to the neuron and its associated relational(experiential) linkages, or ‘relationals’. Detailed later, these oftenmake use of the event queue memory 14 to handle the implications oftheir connections.

Initial Activity Upon Awakening. When awakened in the morning, therested mind (that is, the context pool 10) is usually quite empty.Thoughts and cares of the past day are gone, or are so diminished as tonot be readily recalled. Fragments of sentences, fleeting observationsand incomplete or illogical ideas of the previous day have been purged,the mind uncluttered. This is the context upon awakening.

Daily activity in this brain emulation begins in a similar way. Theinitial tendency is to resort to routine, established lists of actions,usually by the timed fulfillment of events from the event queue 14.Activity can also be started by other external means in both human lifeand in this brain emulation. Table 5 lists some example ways thatactivity begins in the morning, but the list is of course by no meansinclusive:

TABLE 5 Example Start-of-Day Activity Indicators Event ActivityInitiated Hungry for Breakfast For the human, some form of routine thatis normally undertaken, even if only the process of waking up, gettingdressed and eating breakfast. Such a simple process is still a learnedlist, equivalent to one stored in the task list memory 13, though italso may not be consciously present in the mind. If nothing else occursduring the initial state of fogginess, the physical body soon makesknown its need for food, and that initiates a tentative routine. If theemulated brain is connected to a robotic skeleton or vehicle, anequivalent for hunger might be depletion of fuel or electrical charge.Conversation or Sometimes the day is begun by someone else whointerrupts the sleep Telephone Call with a request for attention, askinga question. This is equivalent to wake-up via external communications93, or through speech or visual analyzer 60. The sequence initiated bythe conversation is a part of the thought processes. The sentence may bea command, a question or an observation. Uncompleted List Lists ofthings to be done at the close of the previous day are not always purgedby sleep. They remain part of active context 10 of the brain. Carriedinto the next day with reduced clarity or importance, they are a basisfor the first thoughts of the day. Timed or conditional items emitted tothe event queue 14 may also be waiting.

Any of the above conditions places blocks of neuron references that takethe form of sentences, event-based commands and other information to beprocessed. One skilled in the art will recognize that theanalyzer/correlator 30 can be implemented as hard-coded logic, a form ofcommand interpreter, or as an embedded processor without altering themeans of this disclosure.

Outcomes of Analyzer/Correlator 30 Activity. As a consequence of itsoperation, analyzer/correlator 30 may include any of the activities ofTable 6. The list is indicative of the types of outcomes and is notall-inclusive, but may be extended for the convenience ofimplementation. One skilled in the art shall realize that this does notalter the means of this patent.

TABLE 6 Outcomes of Analyzer Activity Action Description Fire a NeuralIn context pool 10, initiate (or increase) the firing of a neuron foreach Reference new reference to it. Multiple references in the contextpool 10 to the same neuron thus increase its influence. Reinforce NeuralNeurons in t reinforcement memory 11 that have been freshly ‘Keep’ Countreferenced are reinforced. Their time-weighted reference (‘keep’) countis maintained with the neuron in memory 11. Decay 21-day Periodically(e.g., during sleep intervals) decay the ‘keep’ count for all Referencesneurons in the reinforcement memory 11, to enforce the need forreinforcement of learned information. Create a Permanent Neurons inreinforcement memory 11 that have satisfied their reference Neuron countlevel are made permanent by moving them to long-term memory 12, updatingtheir references, and removing them from short term memory. Initiate anEvent Certain conditions, particularly due to neuron relationals, andsome types of sentences, cause events to be queued to the event memory14. The queuing is normally for execution after specified delay,awaiting the meeting of the conditions pending. Ask a Question Basedupon need for more information, ask a question, formatting and emittingit through interface 98. Perform I/O or Initiate appropriate motor skilllists or handle computer-like I/O related Motor Skills to theapplication. Update a State Update relevant state parameters 22 basedupon changes in internal Parameter conditions created by analyzer 30.Trigger Other Neural Initiate action in other blocks such as the tasklist memory 13, to Blocks initiate motor-skill activity or to performmemorized steps. Decayed-Neuron When firing value for a neural referencein context pool 10 has been Removal decayed to zero, remove thereference from the context pool. Neural Reference Periodicallythroughout the active day, neural references in context Aging pool 10are aged, reducing their influence. This aging is accelerated duringperiods of sleep. Conjecture Clutter Commands or references created bythe conjector 70 are correlated for Removal relevance, and discarded forlow relevance to the target subject(s). Dream Clutter While awake,information and command fragments from dreamer 75 Removal are rapidlydecayed. During sleep periods, perceived accuracy of these items isincreased and treated as ordinary and factual information, butmotor-skill related commands are suppressed. Expand Fragment Command theconjector 70 to expand a sentence fragment into the closest equivalentfull sentence.

Besides the items of Table 6, analyzer/correlator 30 maintains andupdates numerous lists, such as present subjects of conversation orinquiry, the status of pending answers to questions issued, maintenanceand completion status of motor skill activity, and the like. Its primarysource of information and commands comes from the present contents ofthe context pool 10.

Context Pool Commands. Within context pool 10, information and facts arestored in the generic form as neuron references, neural indices. Bothstate parameters 22 and context pool commands are encoded as dedicatedlower values of neural indices. The commands are variable in length,with their index followed by length and supporting information.

Many synthesized commands derive from the parsing of sentences bylanguage analyzer 50. Sentences may be distilled into multiple commands,each complete with neural references. Implied subjects, verbs or objectsare resolved with references to relevant neurons. For sentences withmultiple subjects, verbs or objects, the sentence content is replicated,with one copy per item in the subject list, for example.

Some commands found in context pool 10 are given in _Ref90637160˜. Thelist is exemplary and not exhaustive. One skilled in the art willrealize that the list may be extended without altering the means of thesystem of the present disclosure.

TABLE 7 Example of Context Pool Commands Command Remarks Initiate MotorSkill From a command or a list item Await Completion Suspend topicactivity, awaiting completion. Await Factual Answer Question was askedthat expects factual information. Await Affirmative Answer Question wasasked that expects a yes/no answer. Seek Information Ask a question toresolve ambiguity or missing information. Correlate Answer Processanticipated answer Initiate Definition From definitive sentence ExecuteCommand From imperative sentence Repeat Until Condition Perform aniterative operation or analysis. Note Declarative Handle declarativesentence or observation, setting relevant expectations. Note ExclamatoryHandle exclamatory sentence, updating relevant emotional states.Update/Add Topic Refresh list of topics and update relevance of the listitems. Update the Update the list(s) of who is speaking (speaker), whois Communications Triad being spoken to (target) and the object(s) ofconversation. Note Accusation Handle accusatory statements, updatingemotional state and emitting conditional events to queue 14 to prepanswers to implied questions. 231 Declarative Command to handle state ofbeing, remarks or commentary 232 Imperative Command to self to dosomething 233 Definitive Command to define something 234 InterrogativeCommand to respond to a question

For convenience, all data structures in the context pool 10 look likeneuron references.

Execution commands are always flagged by their source, such as a speechor grammar analyzer, the Analyzer or Correlator_30, the Conjector 70,Dreamer 75 and so on. The Analyzer 30 later considers the source whenapplying the command during its thought or decision processes. Exemplarycommands from semantic analyzer 50 are given below, these particularones being based upon sentence types.

Declarative 231 is an instruction to consider a present condition aboutthe subject. It may also be a part of an experience process, ultimatelyculminating in the creation of a neuron-to-neuron orneuron-to-state-parameter relationships. This command is usually createdby the parsing of a sentence, but can also be created by thoughtprocesses within analyzer 30.

Declaratives may result in a remembered relationship, in time and withreaffirmation, and through conjector 70's action. That is, declarativesare ‘taken with a grain of salt’, and consider confidence in the sourceof the observation. They differ from the definitive 233 in that thelatter is already presumed to be a source of facts, and only thereliability of (confidence in) the information needs to be confirmedbefore remembering it.

For example, “Four cats are sufficient to eliminate mice from largebarns,” is a declarative that proposes how many cats it takes to get thejob done. Before analyzer 30 assumes the statement to be factual andremembers it, it will consider its confidence in the source of theremark, and whether or not the information is reaffirmed.

Imperative 232 instructs analyzer 30 to the brain emulation to dosomething, such as to consider a proposal, pay attention, recallsomething, or to conjecture an answer to an issue with insufficientinformation. It is a command for action of some type, directed towardsthe brain emulation.

A command such as ‘Come here!’ must be evaluated in the present context.It implies activation of a motor-skill list to begin physical motion,and targets the location of the speaker. The latter may not be in thecontext pool 10, but is maintained in a state parameter 22. In thiscase, analyzer 30 directs the motor skill via task list 13. It can then,for example, issue an await-on-completion event 142 and dismiss thecommand from memory. It will later receive a completion message (or anotation that it encountered a brick wall or other impediment tocarrying out the instruction), closing the command.

Definitive 233 indicates definition of a fact (in reinforcement memory11), and may include auxiliary conditional relational information.Example, “A cat is an animal with four paws, of which the front two arecommonly called forepaws,” is a compound statement. The statements sharea common subject, and have separate definitive 233 (“A cat is an animalwith four paws”) and declarative 231 (“The front cat paws are commonlycalled forepaws”) clauses. Semantic analyzer 50 separates the compoundinto separate commands for each clause.

Declarative 231 portion, “A cat is an animal withfourpaws,” definesthese neurons if they are not already known: Cat, Animal and Paws. Evenif the meanings of Animal or Paws are unknown, they can still beremembered, and the suitable relationals later formed between them.These are all recorded in reinforcement memory 11, if not already thereand not known in long-term memory 12.

If already in reinforcement memory 11, their existence is reaffirmed toencourage possible permanent recollection. If the veracity of thespeaker is high, less time is required to reinforce the facts. If thesystem is in preemptive training mode, these are assumed to be pristinefacts, perhaps from God, and are immediately and permanently remembered.

The declarative 231 portion, “The front (cat) paws are commonly calledforepaws,” also forms a definition, but must be reaffirmed to a greaterdegree than for the definitive clause. (Because parsing has already beenperformed, the explicit subject defined at the start of the sentence hasalready been associated with the trailing clause, too, by semanticanalyzer 50.)

Because ‘The’ is present, the clause is declarative 231 rather thandefinitive 233. This is because the reference is to a specific cat,rather than to the generic cat animal. One skilled in the art is awareof these subtleties of English grammar, and how that grammar may be usedto determine the intention and type of sentence.

Interrogative 234 poses questions and requests. These are normallyinjected into context pool 10 by the grammar semantic parser 50, but mayalso be queries from other sources. Many (but not all) questions aresimply a declarative statement with a question indicated, and are oftenformed by a restructuring of a simple declarative sentence.

The parser 50 sorts questions into those seeking affirmation (yes/no) orseeking specific information, and presents them to the context memory 10as declaratives 231 marked for validation or as an imperative 234demanding an informative response. In either case, analyzer 30 only seesdata constructs for the latter forms, and so marked as questions so thatit can form its response to the question.

Other internal commands are also added for sake of convenience, analyzer30 loosely taking on the form of a von Neumann processor, with the‘program’ being the command stream from the English parser, or fromother blocks.

In communicating with brain emulators that share common memory 12, theiranalyzer 30 can forward ‘digested’ command blocks directly to thecontext pool of this emulator. If communicating with the outside worldvia external interface 98, analyzer 30 reformats the command block intoan English sentence for parsing there, and receives English back viainterface 93.

Neurons and the Context Pool 10. Conditionals expect a specific neuron(or combination of neurons) to be fired. State parameters 20 and 22 arepseudo-neurons, and preexist all allocated neurons. They are treated asneurons, and are assigned the lowest index ID numbers, but have norelational (experiential) links created for them. The ID of every firingneuron (except for state parameters 20 and 22), along with someinformation specific to the neuron, is maintained in the context pool10, including the degree of firing.

Aged neurons in context pool 10 that are no longer firing are eliminatedfrom the pool memory, usually while ‘sleeping’. Neurons that are firingbut are not being reaffirmed or re-fired in the context pool 10 have noeffect, other than to establish the context of the moment. For example,they may be the subject of a conditional test, or may alter thecontextual meaning of a sentence being parsed.

Unidirectional Relationals. Where relationships are unidirectional, arelational attached to the ‘causing’ neuron issues an event, but only ifthe specified condition is true. For unidirectional relationships, Aimplies B, but B does not imply A. In either case, the relationships maybe conditional, predicated on other neurons also firing. Referring toFIG. 10, a relational link 1253 is created within the neuron impacted bythe relationship.

Bidirectional Relationals. Where relationships are bidirectional,neurons or state parameters at both ends of the relational will issueevents. If any conditions specified are not met, no event is fired off.For bidirectional relationships, A implies B, and B implies A. In eithercase, the relationships may be conditional, predicated on other neuronsalso firing. Referring to FIG. 10, a relational link 1253 is createdwithin the both neurons in the relationship, each referring to theother.

Relationals that Emit Events. When a neuron initially fires (or isreaffirmed), analyzer 30 scans its list of attached relationals. Theyare organized as AND-connected lists optionally separated by OR markers.Consecutive relationals are evaluated until one of them fails or untilan OR marker is encountered. If a relational fails, subsequentrelationals are ignored to the next OR mark or end of the list.

On failure, encountering an OR marker resets the failure condition, theOR is ignored, and testing resumes at the relational just beyond the OR.

If the end-of-list is found first after a failure, no event isgenerated. Finding an OR (or finding an end-if-list, with all previoustests successful) implies that all AND-connected relational conditionswere met, so an event is created. Conditional relationals may be flaggedwith a NOT, implying that the converse of the condition must be true forthe relational to succeed.

Other Internal Lists. Analyzer/correlator 30 maintains other lists ofinformation in short-term memory similar to that of the state parameters22, which are also treated as blocks of predefined neurons. These havebeen discussed elsewhere within this patent and include list such as thefollowing:

Topics of Discussion

Motor Activities in Process

Events whose completion is being awaited

Multiple objects to apply sentence to

Multiple verbs applying to the sentence

One skilled in the art will recognize that the above list is by no meansinclusive, and the logical or physical placement of the above lists maybe altered, or the list added to, without changing the methods of thispatent.

Walking the Neural Connection. When a new command is added to thecontext pool 10, it usually contains a reference to a neuron thatrepresents a fact or condition of existence. Usually it will referencemore than one. Each such reference either brings the neuron ‘into thepool’, or reaffirms neurons already in the context pool 10.

Simply referencing a neuron causes analyzer 30 to bring it into thecontext pool, even if not firing very strongly. Some command blocks,such as from a definitive clause, greatly increase the level of firing.Multiple references to the same neuron over relatively short duration,increases the firing level, also, up to the 100% level.

Recognition of a person's face, for example, brings the ID of thatperson into the context pool 10, firing the relevant neuron inaccordance with the degree of confidence in the recognition. (e.g.,“That might be Jackie, over there.”) Shortly thereafter, hearing thesame person's voice increases the confidence of the identification. Thefiring of that person's neuron (ID) may therefore increase from perhaps65% to 95%. Ongoing interaction with that person keeps his ID alive inthe context pool 10.

Correlation of Relational Information. When in-pool neurons fire, otherneurons may be implied by known relationships. For example, Green andAnimal might imply a parrot if either Cage or South America is presentlyin the context pool 10. Otherwise, if Swamp is firing, Alligator mayfire. Analyzer/correlator 30 gathers triggered references into contextpool 10, updating neuron firings in a manner specified by the scaledconnection weight.

For the case of such relationally-initiated firings, firing level iscontrolled by the values of the referencing neurons (e.g, Green, Animalor Swamp), and the weight given in the relational connections. That is,the Alligator neuron will fire weakly if Florida (which might implySwamp) is firing weakly, although nothing else directly activated Swamp.Analyzer 30 effectively acts as a correlator by walking through theconnections of all firing neurons, awakening other neurons as long asfirings are not suppressed by conditional relationships.

Referring to FIG. 7, if Dog 121 and Excitement 122 are both firing(e.g., information inferred from a parsed sentence), references to themare placed into context pool 10. The relationships of FIG. 7 would setexpectations for a dog to bark via neuron 123. Weights 124, which maydiffer from each other, are multiplied by the firing levels of 121 and122, respectively. If the resultant firings both exceed some minimumdecision threshold, the AND operation 125 causes the generic Dog-Barkneuron 123 to fire. A reference to neuron 123 would then be inserted inthe context pool 10, possibly initiating a motor skill event to cause abark, for example. It should be obvious to one skilled in the art thatmany variations of FIG. 7 are possible without altering the means ofthis disclosure.

Again, the analyzer 30 causes any neuron not reaffirmed or re-fired overtime to gradually decrease its firing level. That neuron is then ejectedfrom the context pool 10 if it goes to zero. It is also dumped frommemory if it is still firing but has been there a long time and thecontext pool 10 is full.

The Long-Term 12 and Reinforcement Memories 11. Reinforcement memory isa way-point in the process of learning and remembering things. All newinformation and relationships are established in reinforcement memory11, and it serves as a filter for items important enough for laterrecall. Analyzer 30 handles this process.

The reinforcement memory 11 is a means of eliminating non-essentialfacts, relationships and incidents otherwise uselessly clutteringpermanent memory. The ultimate growth of long-term memory 12 is thenmoderated, keeping the mental processes and memory more efficient.

Much of the information and experience we encounter is incidental andnot worth recollection. For example, paper blowing in the wind isrecognized for what it is, but the incident is too insignificant toremember, unless perhaps the context is the distribution of propagandaleaflets. The latter might be worthwhile musing over. Reinforcementmemory 11 is the interim repository for this information, while itsworth is reaffirmed or forgotten. Analyzer 30 permanently movesvalidated facts and relationships to long-term memory 11, as discussedelsewhere.

The long-term memory 12 and the reinforcement memory 11 share a more orless common format. Allocation of neurons and relationals are handledentirely by analyzer 30, and policies that govern permanent retentionreside there.

Information is validated by analyzer 30 as ‘memorable’ when it wasrepeatedly referenced over a 21-day period, or repeatedly duringexercise of strong emotion or trauma. So validated, the analyzer 30moves it to long-term memory 12. Referring to FIG. 8, associatedrelationals are also moved from reinforcement memory 11 to the long-termside. Both memories consist of the following items:

An ID Table 126

A Table of Neurons 125; and

Other emulator-specific tables

“Other” tables include specialty tables associated with a single neuronand used for recall of motor-skill task lists, aural or visual artifactsor objects, and the like. Their format is specific to the emulator type(e.g., visual, speech or motor-skill) that produces them, but theyfollow the standard processing and correlation rules for ordinaryneurons.

No neuron is special of itself. Rather, it takes meaning and worth fromposition and interconnection with other neurons. For example, a Laptopneuron is meaningless of itself (except for spelling, pronunciation andvisual shape), but has importance because of its relationships toComputer, Portable, and Convenient.

Handling of Idioms for any Natural Language: One skilled in the art isfamiliar with the various methods of parsing of natural languagesentences, and many tools or methods are available to do that. For theconvenience of description, such a system is assumed to exist for‘parsing’, the breaking of sentences into their constituent parts. Eventhe English language is used here by way of example, one skilled in theart will immediately realize that the same general techniques of parsingand the handling of idioms as described here can also be applied toalmost any other human language.

It is a generally accepted technique to have a (perhaps proprietary)description of English, frequently one of a top-down nature that firstdescribes sentences, then their subject, predicate, object and indirectobject, and so on. For example, one could describe a sentence using aformalized grammar in the following manner:

Sentence=Subject Predicate

Subject=Noun or Noun_Equivalent

Predicate=Transitive_Verb Direct_Object, or, Intransitive_VerbIndirect_Object, or, Intransitive verb.

Of course, this system permits the creator of a parser to “drill down”to any desired level of detail, and can be extended as desired toaccommodate all parts of speech. It is generally accepted that suchformalized description of a grammar is then automatically translatedinto some computer language. Example commercial tools that do thisinclude Lingua, Yacc and Lex. Such tools then create computer code (forexample, in C++) for a program that parses a sentence.

It is assumed that one neuron is preferably allocated to each word inEnglish, at least for words presently recognized by the underlyingparser. Such a neuron is merely a place-holder and has no intrinsicvalue of itself, but its importance is its connection to other suchneurons. The system of interconnected relationships between the neuronsis a fundamental part of what constitutes memory for the artificialhuman.

For example, the word muddy can be construed as any of, “to containmud”, “to be mud-covered”, “mud-like”, “unclear” or other concepts. Inactuality, it is not necessary to have multiple neurons for “mud”,“muddy” or “muddily”. Rather, it is sufficient to have only one neuron,for mud, and create associations relationships/associations betweenneurons, for concepts such as muddy boots. In this case, it aconditional link could be created between mud and boot neurons. In thatmanner, simple reference to the term boot can evoke the idea of mud.Conditional relationships can also be created between boot and rain,boot and snow, and so on.

Idioms can be handled in a similar manner. A single neuron can beassigned to represent a multi-word idiom, and associations can be thendrawn between that “idiom” neuron and other neurons whose meaning (andrelationships) are already established. For example, off the wall can bestored as a single neuron (with the text, “off the wall” attached toit), and that neuron can then be conditionally linked to strange,unusual and offbeat neurons.

One skilled in the art will realize that such associations can beunidirectional or bi-directional, and may be conditional orunconditional. For example, mud can be associated with boot such thatreference to boot implies mud, but such that mud need not imply boot. Asdescribed herein, multiple associations may be drawn between a neuronand one or many other neurons. Each specific association is uni- orbi-directional and is predicated on the firing of some other neuron, ornot.

Parsing: Traditionally idioms can be parsed as a sequence of individualconstituents. Ascertaining their meaning from their individualconstituents is extremely difficult, however, if not impossible. Trueidioms show three qualities, all causing difficulty with parsing:

-   -   Non-compositionality: The meaning of an idiom is not a        straightforward composition of the meaning of its individual        constituents. Under the weather has nothing to do with something        being located under weather.    -   Non-substitutability: One cannot substitute a word in an idiom        with a related word. Below the weather is not the same as under        the weather, although both under and beneath are synonyms.    -   Non-modifiability: One cannot modify an idiom or apply syntactic        transformations such as Luke is under the bad weather, or the        weather Luke is under. These have nothing to do with being sick        or not feeling well.

The formal language description, and parsers created from it—normallydeal with a single word at a time. They infer a word's usage both fromcontext within the sentence and by the word's classification, such asmay maintained for that word in the neuron (or elsewhere), asappropriate.

A scanner device is required that can search for either an explicitsequence of words (e.g., “off the wall”), or—failing that—for a singleword. If such a sequence is indeed found, such as might be associatedwith an ‘idiom’ neuron, that neuron is noted and may subsequently befired, turned on. If only a single word of the sequence is matchedagainst a neuron (or an item's text sequence), e.g., “off”, then theneuron for the offword is identified instead.

By performing this two-part lookup, the example word off can be readilyclassified as a single word, or as a multi-word idiom, off the wall.Either such neuron may/will contain associations with other neurons, andthose associations give the final meaning to the word or idiom.

The relevance of the distinction in the above noted lookup process isthat the entire phrase that constitutes an idiom can be treated inprecisely the same manner as a single word, both in the parser and inthe associated Al logic. That is, the implications of an idiom phraseare handled in exactly the same manner as implications of a singleword's ‘neuron’. The context where the idiom (or word) is founddetermines which other connected neurons also get fired.

For example, the neuron for under the weather is likely to be connectedto (and fire) a neuron that implies ‘ill health’ or ‘not-feeling-good’neuron, or to reduce the firing of a ‘sense-of-health’ neuron.Obviously, one skilled in the art will realize that other neurons to befired by the recognition of an idiom is determined both by the contextwithin the sentence and by the present connections (knowledge) of theneural network in which it is implemented.

Using the above techniques, it is not necessary to individually parsethe constituent words of an idiom and then attempt to establishinterpretation of meaning. Rather, the matching of the idiom's sequenceof words establishes the neuron, context and associations that give theidiom its cultural meaning. Idioms have long posed a great difficultywith parsing because of the above three qualities. Without our methodthey will no longer cause this problem. All of this has been implementedat the parsing and neuron-connection level, and without extra-ordinarycare or logic being necessary.

The following sections discuss one specific implementation of emulatorstructure. One skilled in the art will realize that the technology ofimplementation is secondary to the means described herein. Many of theseitems will be tweaked or implemented variously as the underlyingtechnology of implementations varies, such as software emulation, FPGA,gate array, embedded processor, analog relational arrays or opticallogic.

The ID Table. Referring to FIG. 8, every neuron is assigned a serialnumber 127, something of no significance in itself. Each relationalconnection to another neuron uses that unchanging serial number as anID. From the ID, spelling, pronunciation and other relevant informationis obtained.

When memory is implemented as digital memory, the ID table 126 islocated preferably at the base of that memory and consumes apredetermined and finite logical space. It is sized to have one elementfor every possible neuron. In reality, memory can be resized as more ismade physically available, with suitable offsets applied to theresolution value for each ID in the table 126. For each index 127, thecorresponding offset into the ID table 126 contains a neuron's addressin the neuron table 125.

A vocabulary of 30,000 words is an acceptable working size when wordsalone are considered. For some people, up to 300,000 unique words areknown. Each concept, e.g., “off the wall” to be remembered has its ownindex, as do words, remembered events or conditions; each corresponds toa unique neuron record 1250 in the neuron table 125.

Experiences may or may-not have their own index, depending on what theyare and how they were formed. Because of this, it is therefore realisticto have an index table 126 of 8-20 million items or more, for example.

Table of Neurons. Referring to FIG. 9, neurons 1250 are emulated byfixed-size information block 1251, and a variable number of relationalconnection records 1252. The latter may be conditional, predicated uponthe state of other neurons. They may reference the ID indices 127 ofboth their target and conditional neurons. With better-suited hardwarememory technology available, such as capable of directly formingrelational linkages between neurons, these technology-dependentlinkage-pointer structures may be superfluous and may be eliminated orreplaced.

Basic information 1251 may include references to explicit spellings(e.g., a walk-back index to the text-tree for the word), pronunciationexceptions, visual-object descriptors and the like. Certain flags andstart-indices for lexical matters and the like are also included here.

The relational 1252 is a link between two neurons. It may also be a linkbetween a neuron and a state parameter. Relationals may beunidirectional or bidirectional in nature, and may be performed only ifa specified set of conditions are met. Relationals are loosely suggestedby the biological neural dendron.

When implemented in digital memory, it is convenient that relationals1252 are allocated in the space immediately behind the fixed-lengthportion of a neuron record 1251. Normally there a blank space isreserved there in anticipation of relational records insertions. Beforeinserting a new relational 1252, analyzer 30 checks for sufficient roomand, if not, reallocates the entire neuron with greater space.

The length of the relational detail block 1252 is variable, dependingupon the type and number of relational connections made to otherneurons. It not unreasonable that total (digital) memory may consumesixteen (16) megabytes to two (2) or three (3) gigabytes.

Relationals 1252 have an AND-OR organization. AND-connected relationalrecords are grouped together following the fixed-length portion of theneuron.

Referring to FIG. 10, a specific target ID 1256 is generically definedto represent the OR condition, with the remainder of that ‘relational’record ignored. As stated elsewhere in this discussion, certain neuronIDs are reserved for such special purposes as this. Similarly, certainvalues of the weight 1257 are reserved to indicate an INHIBIT condition,and the weights themselves may be negative, to reduce the level ofrecognition, the firing level.

By itself, the relational 1253 is unidirectional. The neuron 1250 it isa part of is fired to the degree that the neuron referenced by target ID1256 fires. However, the firing of this neuron 1250 does not otherwiseaffect the target ID 1256. For example, Grass may imply Green, but Greendoes not imply Grass.

For conditions in which a relationship is bidirectional, analyzer 30creates a suitable relational for each of the two neurons, each pointingback to the other. This is akin in software to a doubly-linked list.

The weighted and conditional influence of this neuron upon another isdefined by relational linkages 1252, of which there may be up toone-thousand (1000) or more for some neurons. Each new experience andrelationship that is learned has a new relational linkage created forit. The garbage collection and management of neuron-relational memoryspaces is discussed elsewhere in this patent.

Initially, new neurons 1250 and relationships are created in thereinforcement memory 11, where they remain until later validated andmoved to long-term memory 12, or are deleted. Relationals 1252 inreinforcement memory 11 may refer to neurons in either memory, but thosein long-term memory 12 may refer only to other neurons in long-termmemory 12. Analyzer 30 tracks allocation, aging, validation, and‘garbage-collection’ processes, as discussed in detail elsewhere.

Other Tables. Besides pure neurons or relationals 1250, bothreinforcement 11 and long-term memories 12 may hold other encapsulatedinformation. These data blocks are treated and referenced as ordinaryneurons, but contain extended structures for efficient later recall ofcompound and complex entities. Details of each of these are discussedwith the description of their relevant neurons.

The neuron process for recognition of sight and sound is byreconstructive correlation, matching a reference image, or sound againsta known object or sound. Memory storage is ‘reconstructive’ in thatactual sampled sounds or pixilated images are not stored. Rather,sufficient information to reconstruct a reference object (for comparisonpurposes) is remembered. Stored images and sounds then consist of listsof object artifacts rather than detailed information on them. The degreeof match or similarity determines the neuron's firing level.

Refer to Table 8 for a list of some common supporting tables. The listis by no means complete, and one skilled in the art will realize thatthere are many ways to organize such information into tables withoutaltering the means of this disclosure.

TABLE 8 List of Some Common Supporting Tables Table Description TaskLists (e.g., These are lists of actions to be taken, to carry outrepetitive or learned Motor Skills tasks. They are specific tosupporting emulators, such as those that handle motor skills or musicalabilities. Task lists are usually coupled tightly to sensory processes,and can be started, interrupted or stopped by the main brain model.Aural Artifacts These are descriptors of basic sounds, including suchthings as phonemes, ADSR rules and the like. They are not complete wordsor sounds. Aural Interpretive This is a list-like set of rules for theinterpretation of spoken speech, Rules and augments thealgorithmic-based lingual processes. Visual Artifacts This is anarbitrary set of visual elements used to recognize more complex objects.The artifacts may include lines at various angles, facial and noseshapes, alphabetic outlines, and the like. They are elements used forthe reconstruction of visual images, of the minimum detail needed toperform image correlation matching. Visual Objects These aredescriptions of complete visual objects, but of minimal detail needed torecognize them. For example, to recognize a specific face, only aportion of the eyes, nose and chin or cheekbone may be required. Thisreconstruction object is connected to the neuron for a specific person,for example, attaching the face to its identity. The connection is donevia bidirectional conditional link.

Recognition and re-creation of visual objects are different processes,and must be optimized separately. Biological function suggests thathumans do not store detail, such as a bitmap image. Yet, they cancertainly recognize a detailed object, and can accurately identify itwhen exposed to it. A correlation template is recreated from the storedtable information and applied to the appropriate correlator. This maybe, for example, a vector skeleton for use by the visual correlator forimage identification. The neuron fires in proportion to the degree ofmatch.

Event Queue and Memory 14. Events are special-purpose commands issued toa queue 14. They are slated for later execution at a specific time,after a specified delay or after a specified set of conditions are met.They are the means by which unwanted looping over information in thecontext pool memory 10 is circumvented.

An event is simply a marker or flag set down to remind the system to dosomething when a specified condition is met. It greatly simplifies thehandling of actions that are asynchronous with each other. When theanalyzer 30 discovers new information in the context pool 10, it mayissue one or more events to the event pool 14. For example, the analyzermay create an event that adds new reference back into the context pool10. It could also issue a conditional event to later force the analyzer30 itself to iteratively rescan the context pool 10, such as may be donefor an analytical temperament such as the Melancholy.

The same mechanism is also used for establishing conditionalrelationships between neurons, or between neurons 1250 and stateparameters 22. Events can be generated by the alteration of stateparameters 22. By issuing events for future execution, the analyzer 30avoids getting side-tracked from the task at hand being worked.

Referring to FIG. 11 and FIG. 4, the event queue 14 consists of aninterpreter 140 and an event list 141. Creation of an event causes anevent 142 to be inserted in the event list. Events 142 in the list 141consist of a command field and other optional fields shown in FIG. 12.The interpreter 140 repeatedly scans the event list 141 for events 142that can be processed. Whether or not they can be processed isdetermined by the conditions and timing fields. The auxiliary datafield, if present, contains information unique to the event type. Oncean event 142 has been processed, it is removed from the event queue 14.

After interpreter 140 has scanned to the end of event list 141, itrestarts scanning at the beginning. If no events 142 are left toprocess, it awaits the creation of a new event 142. One skilled in theart will realize that the event queue 14 can be implemented ashard-coded logic, as a micro-coded processor, a software emulation, anembedded processor, FPGA, ASIC, optical or other technology of choice,without altering the means of this disclosure.

Conjector 70. Conjector 70 proposes decisions based upon incomplete orpartial facts, or facts of low confidence. While the analyzer 30 is themain thinking facility for the emulator, it takes advice and proposalsfrom both the conjector 70 and dreamer 75 blocks. Proposals from theconjector 70 are filtered by clutter filter 40 on the basis oftemperament and personality.

During the processing of sentence data in the context pool 10,analyzer/correlator 30 acts on the sentence block to determine asuitable course of action where appropriate. If it ‘comes up dry’, theanalyzer 30 invokes the conjector 70 to suggest a valid meaning. If theresulting quality of the conjector 70 output is too low, analyzer 30 maydirect the communications interface 98 to ask for clarification. It setsan appropriate parameter flags to await an answer to the question ofclarification.

Conjector 70 output is similar to any normal neuron reference or sensorynerve that is firing at a relatively low level for the topic. Other thanbeing flagged as coming from the conjector 70, output of conjector 70 isessentially identical to data inferred from sentences by semanticanalyzer 50.

The conjector 70 behaves in a similar manner to the analyzer 30, exceptthat it only looks at material in the present context pool 10. It is notbound by the same needs for hard facts as the analyzer 30 is, andeffectively offers subjective information for consideration. Itsproposals are largely ignored by the analyzer, except for cases such asthe following:

-   -   Information is missing or incomplete.    -   Questions posed by the analyzer through the communications        interface 98 are yet unanswered within the expected interval.    -   Overall level of confidence (firing) levels of information in        the context pool 10 is low.

In effect, when answers are not available to the analyzer 30 fromexisting information, the analyzer turns to the conjector 70 to fill inthe blanks.

For its operation, conjector 70 reviews outstanding questions or issues,as defined both in the context pool, supporting tables and appropriatestate parameters 22. Some state parameters 22 rack the present topicalsubject(s), questions being asked, and information presently beingsought by analyzer 30. On the basis of this material, it scans evenlow-firing neuron references and commands within the context pool 10 andproposes (conjectures) answers for the analyzer 30.

Respect by analyzer 30 for conjecture is implied by the weighting placedon it. Proposals are ignored if they conflict with other information, orif better (stronger firing) information becomes available. Conjecturesage rapidly and are soon forgotten from the context pool 10, whether ornot acted upon. The analyzer 30 considers the source of the conjector70's ‘information’ and its levels of confidence (firing levels). It thenestablishes its own need for the proposal, and its own level ofconfidence in the data. Rejected conjecture is immediately deleted.

One skilled in the art will realize that conjector 70 can be implementedas hard-coded logic, as a micro-coded processor, a software emulation,an embedded processor, FPGA, ASIC, optical or other technology of choicewithout altering the means of this disclosure.

Dreamer 75. Dreamer 75 functions as the ‘right side’ in the brainemulation of this disclosure. It peruses neuron references in contextpool 10 and uses different weightings for state parameters 22 than usedby analyzer 30 for its inputs and decision processes.

The dreamer 75 influences the analyzer 30 primarily by injecting firedneuron references into the context pool 10, rather than just structuredcommands such as from the semantic analyzer 50. Where pre-existinginformation in the context pool 10 comes from visual or aural sources60, or from visual neuron correlations, the dreamer 75 may outputproposals in the form of command blocks.

Similarly to correlator-analyzer 30's processing methods, the dreamer 75generates new references and commands based upon existing neuronfirings. However, when traversing the neuron relational chains, lowerregard is given to relational conditions 1252, as in FIG. 9. Theresulting outputs are of low reliability, as indicated by both theirsource and its firing levels. When analyzer 30 is otherwise inactive oris in sleep mode, the dreamer 75 may indirectly alter the subject topicsby issuing events to event queue 14. Due to the ‘noise’ levels involved,the dreamer 75 may rapidly flit from topic to topic. The dreamer 75 alsoremains active when the brain emulation is otherwise in a ‘sleep’ mode.

When subsequently processing context-pool 10 data created by the dreamer75, analyzer 30 does not create new neurons or relationals in thereinforcement memory 11. Upon awakening from sleep mode, the analyzer 30also rapidly purges residual dreamer-generated ‘information’ remainingin the context pool 10.

The dreamer 75 therefore behaves as a ‘movie-maker’ of sorts,unconstrained by relational logic. It creates new ideas loosely based onthe context of the moment, ideas that also have very rapid lifetimedecays. While this firing of neurons is not in a logical or cohesiveway, it still influences decisions and analyses made by the analyzer 30.

Dreamer 75 is algorithmically based, statistically ignoringstrong-firing neurons and applying logarithmic weighting to firingneurons as a part of its own processes. In this way, dreamer 75 perusesthe context pool 10, effectively giving weight to neurons barely firing.

The impact of the additional neuron firings in context pool 10 is thatthe dreamer places greater overall weight on neurons than the analyzer30 would have. During the course of activity, the firing of some neuronswill be enhanced because of the multiple references to those neurons.Analyzer 30 appropriately weights information flagged as coming from thedreamer 75, and continues to apply its normal logic to the data. Whereit is seeking new ideas, it will weight dreamer-induced referenceshigher than it ordinarily would.

Because dreamer 75 operates at lower effective thresholds than usefulfor analyzer 30, it is more prone to ‘noise’ and error than is theanalyzer 30. While its outputs are less reliable insofar as decisionsgo, its purpose is different. During non-sleep operations, dreamerpseudo-information passes through clutter filter 40 where it may berejected by the personality and temperament filters. During non-sleepoperations, the clutter filter rejects more dreamer 75 output byaltering rejection filter thresholds.

One skilled in the art will realize that dreamer 75 can be implementedas hard-coded logic, as a micro-coded processor, a software emulation,an embedded processor, FPGA, ASIC, optical or other technology ofchoice, without altering the means of this disclosure.

Speech and Visual Analyzers 60. The emulated brain of the presentdisclosure may be applied to a mechanical system, whether a skeleton orvehicle, and list-based motor skill learning functions are used.Interfaces from task list handler 13, event handler 14 oranalyzer/correlator 30 can be used to control external hardware. Theseinterfaces can be used to apply specific levels of force, when used withclosed-loop feedback, or a specific mechanical position, with or withoutfeedback.

Sensors used for the feedback systems are determined by the application.For example, placing one's hand on a table requires either a' prioriknowledge of the table height and position, or requires feedback such asderived from the eyes. Suitable sensors might be a pressure sensor forthe nose (so one doesn't bump into a wall more than once) or for thehand. Aural sensors provide feedback to ascertain the proper formationof sounds, such as to sing on key with existing music.

The methods of this disclosure create correlation templates orproposals, visual or aural objects presented for correlation againstvisual images or sounds. Binary search methods are used to select theproper template for correlation, to rapidly determine degrees ofrecognition. The correlation method constitutes a processed sensor, asensor with internal ability to ascertain degrees of recognition.

Non-processed sensors are simple temperature, pressure, humidity orlight intensity measurement devices, whose outputs are simply formattedappropriately for input to an interface. Processed sensors requireinterpretation and possible correlation before they can developmeaningful signals. For example, using any number of algorithms, avisual sensor takes a template image and returns the degree ofcorrelation in the present image. Similarly, processed aural sensorstake a prototype, such as for a phoneme, and return the present degreeof correlation. Phoneme variations may be proposed if a matching wordhas its neuron firing in context pool 10.

Speech and visual analyzers 60 use task list or other memory such as 13to retrieve the next sequential image templates for correlation asproposed by analyzer 30. These are conveyed as present settings of therelevant state parameters 22. For example, some motor skills demandvisual feedback for the recognition of a table, its upper surfaceposition, and the position of that portion of the hand to be placedthere. These separate objects that must be recognized in turn by thevisual correlation processes.

When the table top has been identified, its position must be reported tothe context pool 10, as is the position of a suitable landing site onit, the proper area prescribed by the analyzer 30's intention anddesire. The outputs of visual correlation are conveniently made relativeto the location of the skeleton's eyes, such that correction for handmotion can be made.

Particularly for the visual recognition processes, motor skills requirefeedback for position, rate of travel, distance and the like. From asingle sensor (e.g., a pair of camera ‘eyes’), multiple streams offeedback can be derived, with the information forwarded as command orevent packets to context pool 10.

Visual and aural cues aid in confirmation of recognition, deliveringfeedback for required motion control. These are needed, for example, torotate and tilt the head properly and to then direct the eye yaw andtilt so the detailed center of the foviated vision is centered on theportion of the scene of interest. These matters are handledinterdependently by list processor 13 and visual/aural analyzer 60.

The speech analyzer 60 dumps its output into the semantic analyzer 50 toactually parse spoken material into items suitable for the context pool10 memory.

Obviously, many technologies for such processed sensors exist, as knownby one skilled in the art. The present disclosure permits interactivepresentation of template information with the sensor, in concert withthe functions of this brain emulation. One skilled in the art willrealize that visual analyzer 60 itself can be implemented as hard-codedlogic, as a micro-coded processor, a software emulation, an embeddedprocessor, FPGA, ASIC, optical or other technology of choice, withoutaltering the means of this disclosure.

Memory Garbage Cleanup and Collection. Garbage collection refers to thereclaiming of unused fragments of memory. During this process, thefragments are sought out and objects in surrounding memory are moved upor down, coalescing unused fragments into a larger block. Coalescedblocks are remembered for later reuse.

Cleanup is a catch-all phrase to cover all things that need to be doneto the memory to optimize it. As noted below, it is used to resizecertain areas of memory to optimize usage, reclaiming previouslyreserved space that could better be used elsewhere.

Memory garbage collection and cleanup processes usually involve themovement of information in memory, with suitable updates to indices andpointers to properly reflect the movement.

Expansion of Relational Linkage Blocks. When a neuron originallyassigned and given an ID by analyzer 30, empty area for the relationals1252 is reserved behind the basic neuron information block 1251. Referto FIG. 9 and FIG. 10. As new relationships are formed, relationalrecords 1253 are appended to the end of the above linkage list.Eventually, this free space is exhausted, an there is no room to add therelational 1252, between the end of the present linkage block and thestart of the next neuron. Something must be explicitly done to fix this.

‘Sleep-Time’ Cleanup Activity. Sleep is used to remove clutter fromshort-term memory, half-formed fragments of thoughts, conjectures, andcertain other items of information. This process enables the next day tostart out fresh, just as with a human. It is a suitable low-risk time toperform optimization of memory. During periods of ‘sleep’, the inactivestate of the brain emulator can be used to advantage to handle movementof validated facts from reinforcement to long-term memory 12. Thisprocess leaves unused holes in reinforcement memory 11, which are alsocleaned up.

During the reallocation of the neuron in long-term memory 12, or whenmoving a relational 1252 from reinforcement memory 11 over to theassociated neuron in long-term memory 12, it is possible there is noroom left for the relational 1252. For this reason, a neuron's space inlong-term 12 must sometimes be expanded.

For this, reinforcement memory 11 is scanned to determine what neuronsare eligible for transfer. If transfer would be impeded by lack ofspace, the associated long-term neuron memory record 1251 is resizedupwards.

When available reinforcement 11 or long-term memory 12 has diminishedbelow threshold, neuron space can also be resized downwards during‘sleep’ times, to optimize it. Neurons 1250 with significant free spacebehind them can have some of that space reclaimed. Heuristics determinewhether or not to downsize. Sparse separation of neurons 1250 in memoryis always faster, so reclamation is only done if required.

Incoming information 93. The implementation of deference between twomodeled individuals takes place in analyzer 30. The position of thepresent individual being modeled within a hierarchy of individual,political or institutional structures is also kept in parameters 22.

All information except that from the analyzer/correlator 30 first passesthrough the clutter filter 40, where it may simply be ignored andscrapped. Clutter filter 40 uses personality-specific parameters 22 todetermine whether the composite personality is even interested inaddressing the information, which has been pre-classified. For example,a Choleric temperament is likely to completely ignore human-interestinformation, whereas a Sanguine temperament readily devours it.

The filter 40 is a catch-all area to pass preliminary judgment on data,including judgment of its source. The filter 40 is controlled by anumber of dynamically-changing parameters, including the current stateof patience. When context pool 10 is full, filter 40 drops information,bio-mimetic to someone in the state of “mental overload.”

Preemptive Training. The brain emulation of this disclosure learns overtime, influenced by underlying temperament. Normal human learningprocesses are used by the emulated brain. Nothing is retained inpermanent memory 12 by the analyzer 30 unless it has been reinforced forapproximately 21 days, avoiding an accumulation of ‘clutter’ facts andrelationships. Facts learned are normally interpreted under theinfluence of the root temperament, which has its implicit filters andanalytical processes (or limited analytical processes, as in the case ofthe Sanguine).

The brain emulation may be ‘trained’ by a method preempting normaltemperament-and-time processes, to rapidly absorb facts, control andenvironmental conditions. The process is therefore described here aspreemptive training. It is assumed in this case that the ‘facts’ andrelationships presented are previously determined to be true andfactual, “from God,” as it were.

Preemptive training may be turned on or off at will, externally to theemulator. It can be turned on to affect rapid training of these pristinefacts and relationships, bypassing temperament-related decision stepsand levels of analyzer 30 and clutter filter 40. In this training mode,access is given to state parameters 22 and controls not otherwisepermitted. When training is completed, these may be returned on. Themodified parameters then immediately effect the personality.

When in preemptive training (‘setup’) mode, the entire contents ofmemories, one or all, are selected or all state parameters 22 may becopied to external storage. This has application for both the commercialmarketing of the information as “intellectual property”, and formilitary purposes as discussed elsewhere. Such ‘snapshot of being’ maybe replicated elsewhere and used as the basis for additional training.

Facts and Relationals. Under preemptive training, new facts andpreliminary relationships between them can be defined using declarativemonolog in a text file, or a verbal narrative if a speech analyzer 60 ispresent. These are described in English prose format. The grammar isinterpreted by the English Parser, but it is not filtered or furtherinterpreted by analyzer 30 or conjector 70. Normal processes for grammarinterpretation are followed, but the information undergoes no furthertemperament-based interpretation or filtering. This approach lets thebrain emulation query the trainer for information that is unclear or notunderstood, and the training process becomes similar that of aknowledge-hungry human being.

Religious Belief and Personal Conviction. Religious beliefs and personalconvictions may be established by preemptive training. As with allpreemptive training, the brain emulation will have no idea of why it hasthese beliefs or convictions. Even so, they can be overridden by deep(extended and consistent) normal training, thereafter.

The beliefs are set by a prose-style description in a text file, to beread by the brain emulation. If it does not understand something orconsiders something illogical, it will ask for clarification by thetrainer. The prose can subsequently be altered to preclude that questionfor the future.

There is nothing fundamentally different in the matter of religiousbelief and personal conviction over other types of facts 1251 andrelationships 1252 that may be learned. However, by defining them underpreemptive training, the normal analytical checks by the analyzer 30 forconsistency and factual basis are bypassed, making them an integral partof the emulated brain's basis of understanding. Religious beliefs orpersonal convictions are established they could also be trained(non-preemptively) over extended time.

Specification of Control Parameter Values. The many control parameters22 and their default values may also be preset by preemptive training.This can also include specific emotional responses to be evoked whendefined conditions are met. The result is again that the brain emulationdoes not know why (he) responds that way, but he simply does. This isuseful to preset human-like likes and dislikes for specific things, foraccurate emulation of a person.

Preemptive training is the method by which the temperament of the brainemulation is specified, including both the base temperament type and theupper-level composite of temperaments. These settings will directlyaffect the outcome of responses and decisions made by this emulation.

The time frame over which the brain emulation learning reinforcementoccurs is nominally 21 days, but defaults to somewhat differentdurations on a temperament-dependent basis. Table 9 gives somerepresentative default reinforcement intervals. ‘Permanent’ learningalso takes place during times of emotional stress or trauma, duringwhich the interval of this table is proportionately decreased.

TABLE 9 Temperamental Learning-Reinforcement Intervals TemperamentDuration Choleric 21 days Sanguine 18 days Phlegmatic 15 days Melancholy21 days

When the time is reduced (it does not effect preemptive training), thebrain emulation is more likely to retain trivia and insignificantinformation. After the emulation is turned operational, those presetsbecome an intrinsic part of its responses. They define the settings fromthe present time onward, until altered.

While in preemptive training mode, memories 11, 12, and 13 and othertables may be saved to external storage, upon command. This includesfacts and relationals 1251 and 1252, and relevant parameter settings 22and 20, and their defaults. In short, anything trained can be restoredto the memory it came from. One skilled in the art will realize that themethods of saving memory and parameter states are dependent upon thetechnology of implementation, and that variations in these methods donot materially alter the system of the present disclosure.

When using a brain emulation of this disclosure to model a specificperson (e.g., a foreign national for military purposes), the emulation'smemory and parameter settings can be “snap-shotted” to enable asimulation re-run under new conditions or parameter settings. Anythinglearned between the snapshot and the time of their later reloading islost and may not be incrementally recovered and reapplied, unless it wasalso snap-shotted.

Degreed Deference. A concept that plays a necessary role in humanrelationships is that of deference to another person. Deference is not‘black-and-white’, but exists by degree. Normally the human makesdecisions that suit himself under the present conditions, without regardto other people. However, he/she will have particular regard (deference)to some people, such as parents, bosses, military chain of command andthe like. The brain emulator uses degreed deference to emulate thisimplied relationship. Referring to FIG. 13, the Present-Need-to-Deferparameter 229 provides the weighting.

Multiple deference tables 128 may be created in memory 12, that apply ina specific context 1283 (e.g., military, political, social order,class). All deference tables are chained together using the links such1284 and 1285. The analyzer 30 scans the deference tables to alter atentative decision, if it conflicts with an external command, such asinferred from an imperative sentence in semantic analyzer 50.

Analyzer 30 seeks a deference table matching one or more active contextsof the moment, as maintained in state parameters 22. Finding one, itspecifies the parameter for the rank self-identity. If the subject beingmeasured for deference is another person, that person's ID 200 is usedinstead. The relational comparator 1280 makes its decision as thedeference output 1282. The decision weighting 1296 is further adjustedby the present need to defer 229. Signal 1296 is then used to determineif any decision should be made at all. In this manner, the analyzer 30defers to commands of authority it is subject to, or weights thedecision outcome if the conflicting command was merely a recommendationof external authority.

The deference tables 128 therefore supply a realistic influence byexternal authority upon the brain emulation. When used in a militaryenvironment, for example, a simulation manager in charge of the brainemulator(s) can exert real-time control upon the brain emulations, ifthe manager's ID is placed at the top of all deference tables.

Preemptive training establishes the set(s) of hierarchical tables 128for relationships between this emulator and others (or other people).The same prose-style description is used to describe the ‘chain ofcommand’ and where the current brain emulation fits within it.

Establishing a down-line deference (i.e., a condition where anotheremulator or person should defer to this brain emulation) is permissible.It sets the emulator's expectations of that other emulator or person.Response to a violation of those expectations is dependent upon the basetemperament specified for the present brain emulator, and may also bedefined during preemptive training.

The Implementation of Temperament. Certain assumptions made by any suchmodel of human psychological function, including this one, enable orsimplify the understanding of brain functions. Properly done, theypermit ready creation and implementation of a synthetic brain based onthat model. They may be right, wrong or erroneous, but such assumptionspermit rapid creation of a ‘baseline’ implementation. Such assumptionsdo not effect the overall means of this disclosure.

The FIG. 14 depicts one such assumption, the makeup of compositepersonality. The assumption is made that each person is ‘pre-wired’ atbirth with a specific set of pre-dispositions, one of four basic typeswell known to those skilled in the state of the art. These include theCholeric, Melancholy, Sanguine and Phlegmatic temperaments, ascategorized and defined among the basic tenants of classical psychology.

To these basic predispositions (temperaments) is added a set ofexperiences and training, learned from the environment in which theindividual lives. The from-birth predispositions are collectivelydefined as a ‘base temperament’, as used here. The sum of thattemperament and the set of experiences is used by the present disclosureto define the composite personality.

FIG. 15 depicts another assumption used by the present disclosure andmodel, approximate traits exhibited by the four classical temperaments.The above ‘pre-wired temperament’ 201 of FIG. 2 are replaced by theactual classical temperament names, in FIG. 15 and FIGS. 16A-D.

FIG. 15 illustrates typical traits (largely, but not fully) specific toone temperament type, as indicated above each temperament. FIGS. 16A-Drepresent the composite personalities of people, each based upon one ofthe four underlying predisposition temperaments.

Through experience and training, the personality of a given underlyingset of predispositions may ‘reach out’ to intentionally assimilatedesirable characteristics of the other three temperaments. The result isa broader composite personality. The individual being modeled here, aMelancholy of FIG. 16 b, for example, may embrace decisiveness orleadership traits more characteristic of a Choleric.

Another assumption made here simplifies the understanding of humanbehavior, and the implementation of this realistic brain emulator. It isthat every person has one and only one basic underlying temperament,regardless of past or present experience or training. When placed underemotional or physical trauma, or under extreme pressure, the actions,behavior, interests and decisions made by the person (or emulation) tendto revert to those characteristic of the person's base temperament.

Obviously, other assumptions could instead be made about the origin anddevelopment of temperament and personality, ones which may be equallyvalid. These could be used here instead by way of examples, but do not,however, effect the present disclosure or its embodiments. The aboveassumptions provide a vehicle for the description of the presentdisclosure, and provide a means for visualizing an otherwise complexmatter.

Weighting of Brain Parameters. FIG. 17 depicts the Choleric parameter202 in its relationship to the Propensity-to-Decide parameter 222, notedearlier. The actual value of parameter 222 is the sum-of-products 2421of the current values of all four temperament-controlling parameters,each with its own weight. The values of the weights 2420 applied areselected and fixed in the emulation, but the controlling temperamentparameters may themselves be adjusted as desired.

It is desirable for one mode of operation that all of the fourtemperament parameters such as Choleric 202 have values of 0 or 100%,such that they are mutually exclusive. It is desirable for other modesof operation that the percentages of all four temperament parameters maybe non-zero, but shall total 100% when summed. An example means toimplement this is illustrated in FIG. 17.

It may be convenient, for example to ‘synthetically’ force the sum ofpercentages of the four temperament parameters to be 100%. Using weights2420 given by the example of FIG. 17 the setting of the Propensity toDecide parameter 222 is given by the equation:Propensity to Decide=50%*Choleric+30%Sanguine+15%*Melancholy+3%*Phlegmatic.

By ignoring how the ‘pseudo-neuron’ temperament parameters are set, theymay be treated as normal neurons in a neural network.

A useful assumption made by this disclosure is that human beings (beingemulated) have a root, or base, temperament at birth that gives thehuman certain propensities for behavior. Experience, training and growthmay cause the human to take on selective traits found predominately inone or more of the non-baseline (‘pre-wired’) temperament.

Implementation of Trauma. A part of this disclosure is theimplementation of the human response to emotional pressure or tophysical or emotional trauma. Such response is modeled here, forexample, as the reduction of impact of such experience, training andgrowth, such that the personality temporarily is dominated by the‘pre-wired’ temperament. This is depicted in FIG. 18.

In FIG. 18, the elements of FIG. 17 are augmented by a selector 241,which takes as its output either of its two inputs, one or the other inits entirety, or a percentage of each input as selected by a determiningcontrol input. In this case, the normal operation and descriptiondepicted by FIG. 4 is altered under emotional or physical trauma orextreme pressure, as noted by parameter 230.

In this case, selector 241 is interposed between temperament sum 2421and the Propensity to Decide parameter 222, such that when under trauma,that decision behavior is instead determined by the ‘pre-wired’ roottemperament 201. The base temperament is pre-chosen as one of theoperational set-up values for the brain emulation and is presumablyunchanged for ‘life’, although nothing prevents such change.

Trauma parameter 230 is triggered and set by sensing other parameter orneuron conditions that indicate levels of extreme emotional pressure ortrauma, or physical trauma or shock, for example, trauma 230 isconfigured to automatically decay with time, using a linear, logarithmicrate or other rate to its nominal ‘off’ (unperturbed) state or value. Itis normally triggered by a change of the above conditions and can bere-triggered if the condition is sustained or recurs, and can bedesigned to decay immediately if the condition is removed.

The conditions triggering Trauma parameter 230 are not depicted in FIG.18, but are presumed to exist, and consist of a sum-of-products ofparameters and brain nodes from whose values the trauma can be sensed.

Handling of Gender. The basic methods of FIG. 18 are extended todifferences of activity between male and female people. For this case,processing flow is augmented with additional multiplexor and weightingtables such as 241 and 242. These would be driven by the Genderparameter 209, instead of Trauma 230, for example. Where appropriate inthe decision and thought processes, these additions are incorporated toaccount for gender-related processing differences.

Use in Military or Political Simulations. Because this disclosure iscapable of accurately emulating human behavior, the brain emulationfinds use in many military applications. Using prior means, it isdifficult to obtain accurate predictive modeling of combat forcedecisions, particularly those motivated by religious belief systems andbelligerent political ideologies. In the present environment ofasymmetric warfare, the ability to forecast combatant decisions becomescritically more important. The means of the present disclosure providethis capability. Refer to FIG. 19 and FIG. 20.

Brain emulator 311 as described previously can be configured to receive‘verbal’ input in the form of a text stream 93 and to emitconversational output text 98. By the addition of a TCP/IP interface3112, or other interface such as for the 1553 bus, the brain emulation3110 can be network-connected to a local or remote network 312. Itbecomes a network-connected brain emulation 311. It should be evident toone skilled in the art that many variations of interface 3112 arepossible without changing the system of the present disclosure.

It is now possible to configure a cluster of these emulators together toform a team. In FIG. 20, these are demonstrated as a Battleforcesimulation cluster 310, such as may be used to predictively modelcombatant forces. The same configuration can also be applied, forexample, in an Unmanned Arial Vehicle (UAV) ‘cockpit’ to emulate aconventional flight crew, each individual specifically trained on forhis task role within the crew. It can likewise be applied to an unmannedunderwater vehicle, to make autonomous mission decisions whendisconnected from the host vessel.

When used as a battleforce simulation cluster, a simulation team 315 ofhuman operators can be assigned to upload intelligence to emulators 311to accurate emulate key individuals in the modeled battleforce. As newinformation becomes available on the modeled combatants, preemptivetraining can be used to update the models.

The emulations 311 used in the simulation cluster can use the portconcept of the TCP/IP protocol to restrict conversations amongthemselves. Such specific local-communications ports can be precludedfrom access by other such clusters via conventional internet gateway313. Cluster 310 can then be used to emulate an enemy combatant force(e.g., a ‘Red’ force), an unknown combatant force, coalition or friendly(e.g., ‘White’ or ‘Blue’) forces, secure from each other.

Multiple clusters 310 may be interconnected to form an integratedbattleforce simulation system 31 as shown in FIG. 21. Simulations wouldbe under the overall direction of a simulation director 330. Thedirector 330 can have secure access to internal conversations within thebattleforce clusters 310 by mans of a dedicated encrypted port thatgateway 313 replicates and encrypts the local busses 320. Thisconfiguration permits independent simulation teams 315 to workindependently of each other but under the scenario proposals anddirections of the director 330.

The simulation director 330 can remotely take snapshots of the memoryand brain parameters of all brain emulations in the system 31. By takingsuch periodic snapshots, the simulations can be ‘rewound’ and restartedwith different scenarios, intelligence information or updatedpersonality profiles.

Simulation teams 315 may preferably consist of psychologists and peoplewith knowledge about the personalities, governments or composite forcesthey are responsible for emulating. This disclosure permits realisticinclusion of religious belief, moral convictions (or lack of them),chains of command and authority, and other relevant personal informationrequired for accurate predictive modeling of people systems.

The simulation system 31 may be located in a local region or may bedistributed across the world. Results of such simulations can be madeavailable to the actual warfighters as a part of C4ISR.

Parsing of Human Language

Definitives Versus Declarations

There are many alternative organizations for the process that separatesdefinitive sentences from declarations. This is generally controlled bythe structure of structure defined in the Baccus-Nauer Format (“BNF”)file that describes the natural language (e.g., English).

The Language Definition

The parser itself is created in a top-down description of the language,and the description (a “.BNF” file) is then translated by the Linguacompiler into a C++ class that serves as a parser. At run-time, thatclass parses the sentence in accordance with the language definition inthe BNF file. Incoming sentences are parsed according to thatdefinition, and the constituent parts are pushed onto a stack.

The BNF is written in top-down fashion, such that a sentence is definedas a Subject and a Predicate, while a Subject is a Noun Phrase, whichitself is an optional ‘a/an’ determiner, a set of optional adjectivesand a noun-equivalent. This process progressively defines sentence partsin more detail, and includes all realistic variations that a sentencemay have.

The Parsing Stack

As parsing progresses, information from the sentence is tossed onto astack in a first-in, first-out order. Where the parser has attempted toparse something as a Clause when in fact it is not, all informationrelated to the (suspected) clause is discarded and later replaced by thecorrect data.

For the sake of convenience, significant portions of the sentence suchas Subject, Predicate, Independent Clause and others are bracketed onthe stack by begin/end markers.

Identifying a Definitive Sentence

A ‘definitive’ sentence defines something. The brain supposedlyremembers the definition of a word, and possibly makes associations orrelationships with it. In practice, definition of a word or topic maybegin with a definitive sentence, but the definition is elaborated withdeclarative commentary afterwards.

Generally speaking, it is possible to know whether or not a sentence isa definitive (a “DEFN”) strictly from structure of its grammar. If allsentences were well-formed, it would be reasonable to identify the DEFNentirely within the BNF description of a definitive.

In practice that places significant and unreasonable burden on the BNF.Further, the BNF cannot identify subsequent declarative topic expansionbeing defined as definitive. It must be ascertained in a step to follow.

The parser should be as streamlined and fast as practical. Currently themajority of the process load is caused by efforts to differentiatebetween definitive and declarative statements. A lot of recursion occursas one pattern match is attempted, fails, and another is tried.Additionally, other sentence types calling on these same patterns haveto go through this extra recursion as well.

In the real world, many problems arise within us when we, as people, get“declarations” pushed into our ‘DEFN centers,’ giving ideas more importthan they deserve. Racism, bigotry and hatred seem to all occur when adeclaration gets handled as a definition. I think we need to be verychoosy on what we let come through as definitions. IMHO, the best way tohandle that would be post-parsing. As a note, I believe we would bebetter off erring on the DECL side by missing a DEFN. This seems to beless catastrophic than pushing a false DEFN.

The brain's following parsing system could be used to assistpost-parsing:

-   -   1. All statement patterns get pushed to the stack as        declarations.    -   2. The parser throws clues to the stack to help post parsing        determine how to handle the statement. Modifiers (e.g., all,        some) and determiners (a, an, the) in the subject and verb types        (is, are) are primary elements useful to determine if a        statement is definitive. An interface function can rule out a        DEFN by checking for a set of these conditions. A token can be        pushed saying: a. DECL, or b. POSSIBLE_DEFN.    -   3. Such tokens can be pushed within each independent clause.

Post parsing can more readily look forward within the stack to helpdetermine a DEFN versus DECL, because we are not restricted to any casesor sub-patterns of the statement pattern. This system is more efficient,and in the end enables us to accurately differentiate between DEFNs andDECLs.

Ascertaining a Declaration

Modifiers (e.g., all, some) and determiners (a, an, the) in the subjectand verb types (is, are) are primary elements useful to determine if astatement is definitive. Absence of a direct object is also a possibleindicator of a definitive sentence. The original methods devised todetermine a DECL were:

TABLE 10 Some Conditions for Definitives Remarks Vb Suffix ExamplesPres-Simple Forms: A. ITV Dogs bark. B. IRR-PRES Dogs unwind. C. VosGerund dogs enjoy hunting. E. Vos Adj Dogs act strange. F. Vos Noun_PhDogs resemble their owners. IS-BE Forms: A. Is_Be IRR_PPART Houses arebuilt/broken. B. Is_Be IRR_PRES ers, ed Dogs are forgivers. C. Is_BeNoun_Ph Dogs are animals. D. Is_Be Adj_Ph Dogs are slimy.These are now replaced with the following:

These 3 conditions must be met for the statement to be a possible DEFN:

-   -   Is_Declaration: The statement must parse via the declaration        pattern.    -   !Decl_Deter: This flag is set off by pronouns in the subject,        demonstratives in the subject or a definite article (“the”) in        the subject.    -   (Is_Be∥Pres_Vb): Is_Be indicates the verb is an Is_Be verb.        Pres_Vb is set for all present verbs.

If all 3 of these conditions is met, we possibly have a DEFN.

-   -   1. Dogs are animals.    -   2. Parsed by the DECL pattern.    -   3. No pronouns in the subject (she is blue), no demonstratives        in the subject (that dog is blue), no definite articles (the dog        is blue).    -   4. “is”=Is_Be verb.

Dogs are animals.=POSS_DEFN

If any of these 3 conditions is not set, we have a DECL.

1. dogs are animals.

Stack + 0 BEG_CLS <CLS> 0 Stack + 1 POSS_DEFN dogs are animals. Stack +2 BEG_SUBJ <SUBJ> Stack + 3 T_NOUN dogs (1639) Stack + 4 END_SUBJ</SUBJ> Stack + 5 SUBJECT dogs Stack + 6 BEG_PRED <PRED> Stack + 7T_VERB are (1000) Stack + 8 ACTION_PRES (action in present) Stack + 9VB_PLURAL (plural) Stack + 10 T_NOUN animals (1626) Stack + 11 VB_GERanimals Stack + 12 CONVEY_ONGOING (progressive) Stack + 13 ISNT_CNDX(1626) Stack + 14 END_PRED </PRED> Stack + 15 PREDICATE are animalsStack + 16 END_CLS </CLS> 0Parse-to-Neuron Mappings

Referring to FIG. 22, there are illustrated sample relationalconnections. The following example sentences are parsed (as definitivesentences) and are then used to permanently create neural relationships.The groups of boxes, the Man and the Chase neurons, are relationalconnections stored with the respective neuron.

This example shows how two sentences on the same general topic (e.g.,men), defining what certain men are like. It also demonstrates what/whois known to be capable of belching (whatever that means).

FIG. 22 uses some of the values from Error! Reference source not found.below.

Table of Relational Commands

TABLE 11 Interpretation of Relational Weights Neuron Weight Item IndexRemarks (See Remarks) “how” (See Remarks) “what manner” R_ACTION VerbUsed by any neuron to specify action to take if Neuron relationalsenable. This must be the last relational of an AND set. R_ACTION_F VerbSame as R_ACTION, but terminates a list subset. Neuron R_ACTOR Noun Usedby verb neuron to indicate who fired this Neuron relational set. Thismust be the last relational of an AND set. R_ACTOR_F Verb Same asR_ACTOR, but terminates a list subset. Neuron R_CDX ± 0 . . . 100%Emotion Degree of influence/coupling.: 100% is full Neuron suppression.R_CDX, ± 0 . . . 100% “regularity” 0 == Never, 50% == Sometimes, 75% ==Often, 100% = Always R_CDX, ± 0 . . . 100% “inclusion” 0 == None, 15% ==Few, 50% == Some, 100% == All. If the level of inclusion is 100%,relational is superfluous and may be removed. R_CDX ± 0 . . . 100%Wiring Degree of influence/coupling.: 100% is full Neuron suppression.R_DO Verb Direct Object indicator Neuron R_ELT Noun Membership in aclass (e.g., on ‘dog’ for “dog is Neuron an animal”. R_GAMUT (0-31) AnyNeuron Gamut table of 1-32 entries follow. R_IDO Verb Indirect ObjectIndicator Neuron R_IMPLIES ± 0 . . . 100% Any Neuron % degree ofsimilarity to the target neuron. (“Dogs are animals” ==> 100%, “Cows mayfly” ==> 30%). R_INHIBIT ± 0 . . . 100% Any Neuron % inhibition offiring target neuron, even if other relationals enable it. R_NOT AnyNeuron Complements present composite conditions set. R_POSSN, ± 0 . . .100% Noun % of ownership. (“Dogs mostly have hairy Neuron coats.”);R_PREP Verb Preposition (see examples below) Neuron R_PREP “when” May beBEFORE, AFTER, etc. 50% ==> now. R_PREP “where” May be IN, OUT, UP,DOWN, ABOVE, BELOW, etc.

Examples of Implies and Possession

Other examples of relationships established using the relational recordsof Error! Reference source not found.1 is shown in FIG. 23, particularlyillustrating the R_IMPLIES (100%) case and the NOT (inhibitor) case. Thelinkage codes may be intermixed within either the noun or verb neuronrelationals.

Example of Not (Negation)

Use of negation is primarily an ‘inversion’ operation. For example, inFIG. 24, dogs are established to be animals (via Implies), and to have‘dogpaws’ (via Possn). The constraint is put on dogpaws (via Not) thatinhibits ‘animals’ from turning on unless ‘dog’ is hardly firing.

That is, Not complements (subtracts from 100%) the present recognitionlevel of ‘dog’. If we don't think the object we're looking at is a dog,i.e., the firing level for ‘dog’ is only 20%, use of a Not then inhibits‘animal’.

Sleep-Time Cleanup

For a given neuron, there may be many sub-lists of relationals that areidentical, replicates of each other learned for the same fact re-learnedat a later date. There may also be sub-lists that are virtuallyidentical, except perhaps for a relatively small difference in theweights used.

To condense such sub-lists and reclaim the space, a ‘background job’ canbe run while the brain is sleeping or otherwise not occupied. Thisoperation can go in and remove the redundant linkage, adjusting theweights to other neurons to a suitable compromise value.

The Concept of Gamut and its Use

Gamut is a means of expressing a range of values or a group of orderedrelationals in an ordered list. It includes expression of notions suchas:

-   -   Direction    -   Ranges of Feelings or Emotion    -   Tenses of Irregular Verbs    -   Relative Strengths or Characteristics of Adjectives or Adverbs    -   Relative Merit of Human Senses—Sight, Sound, Balance, Touch,        Taste

In each case, a specific concept or term (e.g., color) is beingenumerated, and set or list of terms expresses the known variants ofthat concept. For example, color may include violet/purple, blue, green,yellow or red in its gamut—and many other variants besides. Each item ofterminology in the list is then assigned a relative value (it may alsobe an absolute measure, such as wavelength). Gamut may also be appliedto certain aspects of mental state, and to emotion. For example, FIG.25.

FIG. 25 expresses directness of communication along a gamut of 0-100%,and defines certain words that imply various degrees of the state ofdirectness. It also illustrates use of idioms (‘to the point’) in theenumeration of gamut. This will be described in more detail hereinbelow.

The Value of Gamut

The value of organizing information around gamut (where appropriate) isthat nuances of expression can immediately be correctly deciphered fromsentences directed at the behavioral brain model. Just as useful is thatit permits the rapid construction of sentences directed at anotherperson, sentences that contain human-like nuance.

In both cases, idioms may be freely used in enumerating gamut. Use ofidioms gives enormously human-like appeal to the outbound part of theconversation. Idioms are word phrases whose meaning is culturallydetermined, a meaning different from that of the individual words usedin the phrase. For example, “off the wall” does not imply removal ofsomething from a wall. Rather, it currently means unusual or strange, inthe positive sense of the terms.

With gamut, there is no need to algorithmically account for us ofspecialized words, but to simply specify that they are part of theenumerated gamut of values that something can take on.

As an example, consider “color.” Color is an adjective whose valueranges from deep violet at 390 nm wavelength to deep red at 710 nmwavelength. The gamut of color includes the list {deep violet, violet,blue, green, yellow, orange, red, and deep red}. Of course, when colorsare mixed with varying brightness of white, or are of lower intensitythan their surroundings, an additional gamut of tints may be added tothe list of colors defined. For example, orange turns to brown if theintensity is reduced, or to tan if white light color is added to it.

As an example of the use of gamut, consider that one system generates anexpression to convey some level of alertness. Suppose that that systemgenerates a phrase as follows—“I am very (expression of alertness) aboutwalking along this path.” This is a situation where a system may havebeen trained such that the presence of the path, i.e., an objectassociated with the system, has some prior danger associated therewithor, in the alternative, some euphoric event. This is basically arelational associated with the particular word “path” which, whenassociated with the particular phrase, especially with the term“walking” would raise some type of alertness, the concept of alertnesshaving varying levels of expression that extend over a range ofexpressions. The alertness could range from a rather unconcerned levelof alertness which would be basically inattentive, i.e., there was noconcern about traversing the path, or it could be one of high alertnessor urgency. The concept of alertness could be neutral with positive andnegative ranges disposed thereabout. The relationship with the pathwould define how this is expressed by the one system. For example, thephrase could be “I am very bored about walking along this path” or itcould be “I am very frightened about walking along this path.” However,the level of danger or alertness would be expressed in a percentage froma low percentage, i.e., inattentive or unconcerned to a high level,i.e., very frightened. The danger level would result in the variouslevels of alertness being selected between the extrema. An example ofthe gamut is set forth as follows for alertness:

-   alertness,n,adj(agam(“solidly asleep”=0, sleep=5, inattentive=10,    “out of it”=20,\bore=30, weary=35, relax=40, docile=50,    warn=55,\concern=60, apprehensive=65, fear=68, trepid=72,    attentive=76,\alert=80, energy=85, urgent=90, fright=95,    horrify=100))

alertness (1036): GAMUT ADJ, 19 CDX solidly asleep (1037) CDX sleep (7%)(583) . . . CDX relax (39%) (945) CDX Docile (803) CDX warn (55%) (1022)CDX concern (62%) (771) . . . CDX energy (84%) (817) CDX urgent (91%)(1019) CDX fright (94%) (846) CDX horrify (100%) (871)

The above noted table is associated with an alertness neuron, defined asthe neuron “1036” that is defined in the system. Each neuron in thesystem is defined by a number which is disposed in parenthesis adjacentto a particular term. For example, the alertness neuron would have agamut table (ordered list) associated therewith which would have, forexample, a rating of 91% for the term “urgent” which is associated withthe neuron “1019.” This is a forward relational whereas the urgentneuron, neuron “1019,” would have a back relational back to thealertness neuron “1036.” These gamuts are basically tables of orderedlists in the relational portion of a particular neuron which constitutesthe root neuron for the gamut and these are created during population ofthe system. This example will be described in more detail herein below.

Applications

Many places exist in the brain model where gamut is very useful. A fewsuch applications include:

Emotion—E.g., State of feeling: like I'm dead, awful, pretty bad, nottoo good, a bit down, okay, pretty good, good, great, awesome, fantasmic

Irregular Verb Tenses—E.g., Do, did, done

Quantifiers—E.g., None, afew, several, some, lots, a lot of, all

Sequences—E.g., out to the front, off to the left, off to the left, offto the rear, off to the right

Deference Chains—E.g., Army: private, private first class/PFC,corporal/Specialist 4, sergeant/buck sergeant, staff sergeant, sergeantfirst class, sergeant major, command sergeant major

Relative Adjectives and Adverbs—E.g., Least, a lot less, less, some,more, a lot more, most

Many other useful applications of gamut also exist, besides.

Implementation

There are many ways to implement gamut, as one skilled in the art willknow. In each case, implementation should include the following:

List of Gamut Values in association with a root neuron, Each Independentof the Root Owner

Handling of Multi-word Phrases or Idioms.

An example of this is that color, orange, off-white and red are allcommonly-used terms in contemporary English. Any of them must be parsedand recognized for what they are in a sentence, and all of them must beavailable for the formation of ‘out-bound’ sentences in reply. In thiscase, color is the ‘owner’ or root neuron of the gamut, and the names ofthe colors are independent terms in their own right, but all point tocolor, the owner.

Words for a brain emulation can be stored and accessed in a variety offormats, such as linear lists, sorted tables with binary search, hashtables, and the like. The exact used is less important than is the factthat once a word is looked up, immediate access is provided toadditional information about that word. That is, a lookup of a wordsubsequently provides fast access to information about the word.

Idependent Access

Every word in the brain's present vocabulary is independent of eachother, for purposes of both parsing and construction of new ‘out-bound’sentences. Of course, ‘red’ implies a color to us, but the term colorneed never be looked up or referenced, for red to be used in a sentence.In this manner, access to both the root gamut owner word and the wordswithin the gamut are essentially independent of each other.

Multi-Word Phrases or Idioms

It is appropriate to use word phrases that are culturally defined(idioms) as a part of the expression of gamut. For this reason, the wordstorage and lookup mechanism (‘back box’) must be able to look up idiomword-sets as well as single words. That is, the phrase, “to the point”is just as valid a means of gamut expression as is the single word“blunt”.

It would take a lot of work and logic to make associations for theconstituent words of an idiom, welding them in some manner into a uniquemeaning separate from their original separate meanings. The manner ofstorage and retrieval should therefore make complete provision for suchword-groups as a set, returning the identity of a single item (neuron)that represents the meaning of the multi-word idiom.

This means that multi-word handling applies both to the storagearchitecture and to the lookup means chosen. For lookup, the parsing ofa sentence should yield a single item (value) for the entire multi-wordphrase that constitutes the idiom. That is, each known idiom can have aunique neuron identified with it.

Association of Enumeration with Owner

The identification (be it a pointer, index, text name or neuron ID) ofthe gamut's root owner—color, in the case of preceding examples—shouldbe stored somewhere in the data associated with each word of the gamut.Red, green, violet and yellow must each retain the ID of color with it.It must also have a marker that indicates it to be part of a gamut ofvalues. Each color need know nothing about other members of the gamutenumeration for color, only that it is a gamut value and what the rootname/identification of that gamut is. If other members must be known,that must be via the root owner's ID in some manner.

Owner's List of Enumerated Words

Unlike the members of the gamut enumeration, the gamut's root owner mustmaintain a list of all constituent members of the gamut in an orderedlist. For example, color must contain a list of all members of the colorgamut, again as a list of identification means such as the member used.

Example Organization of Memory for Gamut

Gamut may be applied to any measurable quantity, color, for example.When the current numerical value of color is known, the appropriate nameor terminology to express that color can be quickly chosen by indexinginto the list of gamut identifiers.

For sake of variety, a small random number can be added to the presentnumerical value and the sum used as the gamut lookup index instead. Aconvenient place to store the gamut tables in the brain model of thisinvention is in the relationals list that is associated with everyneuron.

If memory is organized properly, all items in the list imply color, andeach implies a specific quality of color. To large measure, theenumeration terminology can be used independently of the root word thegamut applies to. Violet can be recalled and used without regard towhether or not color is referenced as a word in the present sentence orcontext.

A depiction of storage layout that permits this is shown in FIG. 26, andapplies to the gamut for directness illustrated in FIG. 25. Only two ofthe example gamut enumerations (‘oblique’ and ‘inarticulate’) are shown.The same layout scheme also applies to color and its gamut enumeration.

Core Word+List of Expressions for that Word

FIG. 26 shows an example ‘gamut owner’ or ‘root’ for the gamut process.In this case, it represents a hypothetical mental state within the braindefined here as directness, meaning the directness with which the brainwill communicate at the moment.

The directness (neuron) is looked up by name “directness” or may beotherwise known by a'priori knowledge or design. Associated withit—through training—is a set of terms that convey the gamut ofdirectness, ranging from completely inarticulate to very blunt.Regardless of the precise terms used to enumerate the levels of gray inbetween these two extremes, each enumeration contains an associatedvalue. The examples used her use a range of 0-100%.

The gamut table happens to be stored in the neuron area set aside forrelationals, weighted connections to other neurons. The term‘directness’ might imply the word should be given noun typage. The factthat it contains a gamut list is recorded as a type-related flag bit orcode.

The gamut table itself contains a list of identifiers that permit accessto words/neurons that define a specific level of directness. When at alater time it is desired to communicate the present mood of directness(which, for example, might have a value of 60% defined external to theneuron), it is necessary to first look up or recall the term directness.The hardware or software implementing the brain then looks for theclosest entry in the table, which happens to be 70%, implying the levelof directness is ‘concise’.

Independent Values for each Word in the Gamut List

The gamut table itself contains a list of identifiers that permit accessto words/neurons that define a specific level of directness. Thespecific level of directness is recorded in the gamut table along withthe identifier, and is duplicated as an internal value stored with therelated enumeration word. The duplication is not crucial toimplementation, but can save additional search time in many cases.

Independent Access of Words, Phrases or Idioms

All constituent elements of the gamut list—and gamut list owner—can belooked up in memory using the system of implementation chosen for thememory storage area. This means that no secondary lookups are necessaryfor the parsing or understanding of a word within a sentence. Becausethe gamut table already contains a list of identifiers for theconstituent parts, no lookup is needed for any of them, because theindex or identifier can directly imply an address or location in thememory where the data is contained.

Referring now to FIG. 27, there is illustrated a more detailed diagramof a particular root neuron 2702 associated with a particular term. Thisterm may, in and of itself, be related to other terms not associatedwith a gamut. These are the relationals. They are termed R1, R2 and R3,for example. However, one of the relationals for this particular neuron2702 is a gamut, i.e., this provides information to this particularneuron 2702 that it has many shades of meaning, which particular shadesof meaning are selected through some type of gamut input from anexternal source. Each of the relationals, R1, R2 and R3, for example,have a relationship or a link with a particular neuron, neurons N1, N2and N3 respectively. The gamut relational is associated with a table2704 which is part of the neuron. This has N relationships associatedtherewith, which each of which gamut relationship has a percentageassociated therewith from 0% on one side to 100% on the other extreme.With the gamut input in a percentage value defining which of the gamutvalues is to be output, this will define the relationship to one of theneurons associated with particular gamuts. For the gamuts G1 and G2, forexample, there are provided neurons N_(G1), N_(G2), etc., up to neuronN_(G3). As an example of a particular neuron, consider the followingtable for an anticipation neuron. In general, gamuts are used primarilywith adverbs, adjectives and verbs. The neuron anticipation isassociated with two different gamut tables, one for an adjective, andone for an adverb. The word type will define which of the gamut tablesis utilized. In this table, there is provided about how many gamuts areassociated with a particular gamut table. In this example, there aretwenty-one relationals associated with each gamut table, 21 for theadjective or type N, 21 for the adverb type. Only a portion of these areillustrated.

anticipation (1045): GAMUT ADJ, 21 CDX traumatize (999) CDX dread (7%)(805) CDX nothing good can come of this (1046) (13%) . . . CDX forebode(42%) (841) CDX trepid (42%) (1001) . . . CDX seek (81%) (545) CDXanticipate (84%) (729) CDX strongly anticipate (91%) (1048) CDX antsy(100%) (730) ADV, 21 CDX traumatize (999) CDX dread (7%) (805) CDXnothing good can come of this (1046) (13%) . . . CDX nervous (33%) (917)CDX constrain (36%) (776) CDX forebode (42%) (841) . . . CDX search(81%) (961) CDX anticipate (84%) (729) CDX strongly anticipate (91%)(1048) CDX antsy (100%) (730)

As an example of the use of the gamuts, suppose that a particular wordis determined to be an adjective and it is associated with theanticipation mood. Further, assume that an external source hasdetermined that the anticipation level or mood is at a level ofapproximately 40%. The system would look at the input and then select asto the particular mood to assert in a sentence or otherwise to be theone of “forebode.” If a sentence were being constructed, this would bethe word that this particular system utilizes. The input that definesit, i.e., the percentage input, would be one that is learned withexperience. For some reason, the context of the sentence, as describedabove with respect to the “path” example, would define for a particularsystem what that percentage value would be. However, this could bedifferent for different systems.

Referring now to FIG. 28, there is illustrated the interaction of twosystems for this example of the use of “forebode.” With respect to FIG.28, one system, a system 2802 generates a sentence referred to as thesentence “I have a foreboding feeling about this.” This sentence iscommunicated to a second system 2804. However, the system 2802 has agamut table 2806 associated with the anticipation neuron which has allof the terms for the adjective associated therewith and which have apercentage value from the lowest level of anticipation to the highestlevel of anticipation. As noted herein above, this example, theforeboding gamut for anticipation is at a 42% level. The input level ofanticipation is 40%. Therefore, the way this would be expressed, wouldbe by using the term “foreboding.” This would be communicated in thesentence and the second system 2804 would review the word forebodingwhich would be directed toward a neuron 2810 which would have a backrelational to an anticipation neuron in the system 2804 which would haveits own gamut table 2812 for the adjective form of “forebode” that wouldhave possibly a different level of anticipation associated therewith.This could be to the fact that it was populated differently, traineddifferently, etc. In this example, it might be that the anticipationlevel is 10% which means that this term “forebode” is associated with amuch lower level of anticipation. Therefore, the one system 2804 wouldview the sentence that was constructed by the system 2802 as having alower anticipation level than the system 2804.

Parsing and Gamut

The language parser is the device or object that reads sentences (e.g.,English) and gleans purpose and intent from their structure. So-called‘natural language’ parsers operate by breaking sentences into theirconstituent parts in a top-down fashion. Sentences are broken intoclauses, and clauses into subject and predicate. The latter are in turnbroken down into phrases, special structural words, and lowest-levelparts of speech, the words themselves.

The words are looked up in a manner appropriate to the neural model orbrain system chosen. In the case of the disclosed system, many words arespecialized and treated as nerve-like interfaces to the outside world.Even with these specialized behaviors, each word is looked up by theparser in the brain's memory, and the related semi-permanent neuron IDfor it is returned to the parser.

In the disclosed behavioral brain model, a definition of English grammaris first defined and then fed to a software tool. The software tool inturn compiles the definition of English grammar and synthesizes acomplete parser for the English language. Such tools exist in thegeneral marketplace.

The definition utilized in this disclosure is coded in a ‘BNF’(Baccus-Nauer Format) file. The complexities and vagaries of the Englishgrammar, structure and lexical format are defined in that BNF file.Should there be a problem parsing a particular sentence, it is the BNFfile that must usually get corrected to handle the case.

For the sake of gamut, the primary requirement placed on the parser isthat it be able to treat multi-word phrases and idioms as equivalent tosingle words. In the disclosed behavioral brain model system, each wordrepresents a single neuron. Idioms and multi-word phrases (such as ‘tothe point’) are likewise treated as single neurons.

With this proviso in place, it is then very easy to define gamut listsusing the vernacular of the current generation, if desired. When theparser encounters any such multi-word item or idiom, it finds it as itsown word-set with its own neuron ID. Such multi-word phrases or idiomscan be either taught—such as by using definitive sentences—or pre-loadedto the brain from a specially formatted list appropriate as adictionary. An appropriate place for these in the Neuric behavioralbrain model is in a special file of structural English words thatrepresent essential knowledge.

The Training of Gamut

The disclosed behavioral brain model uses a file of informationformatted for easy ingestion into the brain. (There also can be provideda second specialized parser that extracts and remembers data from thisfile.) The file primarily contains ‘structural words’, specialized wordsthat do not change much from generation to generation. It excludes mostnouns and most verbs.

Verbs that remain in that initial-word training list include theirregular verbs (do/did/done), various forms of to be, and other suchverbs that hold special places in English grammar. Similarly, adverbsand adjectives relating to human senses are all retained there, as areall the prepositions. Essentially, any word needed to relate to ‘theworld immediately around me’ is kept there.

This initial-word training list is a natural place to put common idiomsand gamut tables. Doing so also permits ready tweaking of relationshipsthat are not black-and-white in English. For example, one can define agamut of ‘how much’ that includes: none, a little, some, quite a little,quite a lot, gobs and mountain of. If ‘some’ is an indicator of ‘an okayamount’, it may not be obvious that quite a little should indicate morethan ‘some’ implies. Gamut—and gamuts pre-stored in the structural wordstable—permit one to readily tweak the relative position and orderings ofsuch words.

It is entirely acceptable that two person's opinions of the ‘proper’order might be at odds with each other. The system of gamut permits usto readily handle that case.

Gamut Tables—Specifying Relative Values of a Set

Many words form ‘sets’ of similar items whose value lie along a scale ofvalues. For example, adverbs expressing how often or how many might beexpressed in the following way:

-   -   How Often: Never=0%, seldom=15%, sometimes=40%, regular=50%,        often=75%, always=100%    -   How Many: None=0%, a few=15%, some=50%, a lot=60, most=75%,        “almost all”=92%, all=100%

Certain tables that define these relationships (which are named gamuttables) can be used to define both the terminology and neurons thatdepict the nuances of value over the ‘gamut’, range of possible values.The same concepts and table types apply to adverbs, adjectives,irregular verb (tenses), emotion, and tables of deference (e.g.,military, political or social rank, which are non weighted orderedlists).

Gamuts of Emotion

Some 250 or more separate emotions can be readily identified, some ofwhich are mutually exclusive and some of which describe markers along arange of values (i.e., a gamut of emotions). That set of emotions hasbeen divided into some 30+ specific emotions, each having itsindependent gamut for which certain values are named.

Other groupings or divisions of emotions could also be used withoutaltering the concept being described here. Additionally, other emotionsexist that are not reasonably described using a gamut-based enumeratedset of names.

The value of the gamut approach is simplification of emotions intoclosely-related categories that the brain model can describe to aninterested party. Rather than stating the percentage of emotion it feels(i.e., 0-100%, which would be silly and stilted), it can now use theconventional terminology that describes its present feeling. This alsopermits the use of idioms (well being or scatter-brained) to succinctlycommunicate nuances of emotion.

Finally, the gamut concept is fully compatible with the system ofweighted relationals used with individual neurons in the disclosedbehavioral brain model. Each root emotion can be configured to reserve32 consecutive (preferably the first 32) relational slots to depict thename of a variant of emotion. While 32 slots is a matter of convenience,variable-length lists or other fixed-length list sizes can be used. Theassignment of weight-codes or percentages for the gamut table isdescribed herein above.

Such a gamut of feelings might look something like that illustrated inFIGS. 29 a and 29 b, an example of what a mental clarity emotion'smapping might look like. Like other gamuts illustrated, the choice ofunderlying emotion name and the terms used to describe its intensity aresubject to change, tweaking and additions. The examples are intended tobe illustrative and not precise, and actual values used may reasonablybe quite different.

The ideal choices for nomenclature and would be mutually exclusivewithin a given emotion. The challenge is to properly identify what namesare simply enumerations of an underlying emotion, and what thatunderlying emotion might properly be.

Obviously, the intensity of a given emotion could vary from 0-100%, oreven −100% to +100%. While either can be preferably used, the range of0-100% is utilized, with 50% being a nominal emotion with “nothinghappening”.

The following table shows example gamuts of emotion. The percentageassignments happen to be loosely based on 3% increments, such that thegamut can be expressed over a range of 32 unique values. (This way, arange of 0-100% can be expressed as a value from 0-31.)

The nomenclature in the table names the representative emotions as‘E_emotion-name’, where emotion-name is the root emotion being assigneda gamut of values. The suffix “,g,e” is a syntax of convenience thathappens to be used in an implementation of the Neuric brain model,although other means can be used to depict the type of value beingdescribed. The numeric values given are values (in percent) thatapproximate the value of the root emotion for which the name applies.

-   -   E_acceptance,g,e(bitterness/bitter=0, rejected=10,        disapproved=15, distant=20, separated=25, suspicion=30,        negative=35, lonely=40, alone=45, indifferent/indifference=50,        tolerance=55, accepted/acceptance=65, friendship=70,        closeness/close=75, connected=80, delighted/delight=85,        approval=90, amazement=100)    -   E_alertness,g,e(“deep sleep”=0, sleepy/sleep=5, inattentive=10,        “out of it”=20, boredom/bored/bore=30, weary=35,        relaxed/relaxation/relax=40, docile=50, warn/warning=55,        concerned=60, , apprehension/apprehensive=65, fearful=68,        trepidation=72, attentive=76, alert/alertness=80,        energetic/“emotional energy”=85, urgency=90, fright=95,        horror=100)    -   E_amusement,g,e(dazed=0, grief=10, shocked/shock=20,        “un-amused”=30, serious=35, “not funny”=40, indifferent=50,        warmed=60, humored/humor=75, amused=85, mirthful/mirth=100)    -   E_anticipation,g,e(trauma=0, dread=10, frig htened/frig ht=15,        warned=20, suspicious/suspicion=25, negative=30, nervous=35,        constrained=39, trepidation/trepid=42, boredom/bored=45,        ennui=48, commonplace/“common place”/“common-place”=50,        intrepid=55, expectation=60, desirous/desire=65, optimistic=70,        persistence/persistent=75, seeking/seek=80, anticipation=85,        “strong anticipation”=90, antsy=100)    -   E_composure,g,e(hysterical/hysteria=0, terror=5, shock=9,        suffering=12, “torn up”/“torn-up”=15, frightened/fright=19,        worried/worry=22, alarmed=26, anxiety=29, agitated/agitation=32,        suspicious=35, troubled=38, confused=41, cautious/caution=44,        sensitive=47, okay/Ok=50, calm=55, reconciled/reconcile=60,        peace=65, competence/competent=70, cheer/cheerful=75,        composed/composure=80, collected=85, optimistic/optimism=90,        cool=100)        In this manner, what are commonly regarded as separate emotions        or states of emotion can be readily depicted with reasonableness        and surety using the gamut system.

In general, the concept of gamut offers a way to systematically definenuances of expression that are commonplace and in daily usage. Itsimplifies the organization and cross-linking of information, facts andrelationships. Gamut is also a perspective and way of approaching theproblem of nuance in human interaction.

1. A method for emulating human cognition in electronic form,comprising: receiving information in a form of a textual or voice inputin a natural language; parsing the received input into pre-determinedphrases based on a stored set of language rules for the naturallanguage; determining if the parsed phrases define a concept and, if so,then creating weighting factors to other concepts in the naturallanguage in an ordered list, the created weighting factors operable tocreate a weighted relationship to the other concepts; and determining ifthe parsed phrases constitute a query and, if so, then using theweighted factors to make a decision to the query.
 2. The method of claim1, wherein the stored set of language rules includes fundamentalconcepts that underlie human personality.